{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "224f85ed",
   "metadata": {},
   "source": [
    "## 5.1.1 mean( )函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a36df6ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>code</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>turnover</th>\n",
       "      <th>turnover_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>华大基因</td>\n",
       "      <td>300676</td>\n",
       "      <td>67.80</td>\n",
       "      <td>68.76</td>\n",
       "      <td>67.00</td>\n",
       "      <td>67.70</td>\n",
       "      <td>59788</td>\n",
       "      <td>410465888.0</td>\n",
       "      <td>2.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>华大基因</td>\n",
       "      <td>300676</td>\n",
       "      <td>67.80</td>\n",
       "      <td>69.55</td>\n",
       "      <td>67.78</td>\n",
       "      <td>67.90</td>\n",
       "      <td>49114</td>\n",
       "      <td>340216880.0</td>\n",
       "      <td>2.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>华大基因</td>\n",
       "      <td>300676</td>\n",
       "      <td>66.98</td>\n",
       "      <td>67.71</td>\n",
       "      <td>66.08</td>\n",
       "      <td>66.36</td>\n",
       "      <td>63286</td>\n",
       "      <td>427489168.0</td>\n",
       "      <td>2.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>华大基因</td>\n",
       "      <td>300676</td>\n",
       "      <td>66.59</td>\n",
       "      <td>67.20</td>\n",
       "      <td>66.20</td>\n",
       "      <td>67.16</td>\n",
       "      <td>43485</td>\n",
       "      <td>294132224.0</td>\n",
       "      <td>1.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>华大基因</td>\n",
       "      <td>300676</td>\n",
       "      <td>66.63</td>\n",
       "      <td>66.79</td>\n",
       "      <td>64.76</td>\n",
       "      <td>64.76</td>\n",
       "      <td>71965</td>\n",
       "      <td>476885488.0</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-26</th>\n",
       "      <td>华大基因</td>\n",
       "      <td>300676</td>\n",
       "      <td>52.45</td>\n",
       "      <td>53.19</td>\n",
       "      <td>52.33</td>\n",
       "      <td>52.85</td>\n",
       "      <td>15377</td>\n",
       "      <td>81365412.0</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-27</th>\n",
       "      <td>华大基因</td>\n",
       "      <td>300676</td>\n",
       "      <td>52.88</td>\n",
       "      <td>52.90</td>\n",
       "      <td>51.54</td>\n",
       "      <td>52.25</td>\n",
       "      <td>23972</td>\n",
       "      <td>124694980.0</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-28</th>\n",
       "      <td>华大基因</td>\n",
       "      <td>300676</td>\n",
       "      <td>51.95</td>\n",
       "      <td>52.44</td>\n",
       "      <td>51.71</td>\n",
       "      <td>51.80</td>\n",
       "      <td>17561</td>\n",
       "      <td>91289511.0</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-29</th>\n",
       "      <td>华大基因</td>\n",
       "      <td>300676</td>\n",
       "      <td>51.68</td>\n",
       "      <td>52.05</td>\n",
       "      <td>51.44</td>\n",
       "      <td>51.70</td>\n",
       "      <td>13485</td>\n",
       "      <td>69845538.0</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30</th>\n",
       "      <td>华大基因</td>\n",
       "      <td>300676</td>\n",
       "      <td>51.69</td>\n",
       "      <td>52.70</td>\n",
       "      <td>51.50</td>\n",
       "      <td>51.69</td>\n",
       "      <td>16905</td>\n",
       "      <td>87552630.0</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>728 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            name    code   open   high    low  close  volume     turnover  \\\n",
       "date                                                                        \n",
       "2020-01-02  华大基因  300676  67.80  68.76  67.00  67.70   59788  410465888.0   \n",
       "2020-01-03  华大基因  300676  67.80  69.55  67.78  67.90   49114  340216880.0   \n",
       "2020-01-06  华大基因  300676  66.98  67.71  66.08  66.36   63286  427489168.0   \n",
       "2020-01-07  华大基因  300676  66.59  67.20  66.20  67.16   43485  294132224.0   \n",
       "2020-01-08  华大基因  300676  66.63  66.79  64.76  64.76   71965  476885488.0   \n",
       "...          ...     ...    ...    ...    ...    ...     ...          ...   \n",
       "2022-12-26  华大基因  300676  52.45  53.19  52.33  52.85   15377   81365412.0   \n",
       "2022-12-27  华大基因  300676  52.88  52.90  51.54  52.25   23972  124694980.0   \n",
       "2022-12-28  华大基因  300676  51.95  52.44  51.71  51.80   17561   91289511.0   \n",
       "2022-12-29  华大基因  300676  51.68  52.05  51.44  51.70   13485   69845538.0   \n",
       "2022-12-30  华大基因  300676  51.69  52.70  51.50  51.69   16905   87552630.0   \n",
       "\n",
       "            turnover_rate  \n",
       "date                       \n",
       "2020-01-02           2.45  \n",
       "2020-01-03           2.01  \n",
       "2020-01-06           2.59  \n",
       "2020-01-07           1.78  \n",
       "2020-01-08           2.94  \n",
       "...                   ...  \n",
       "2022-12-26           0.37  \n",
       "2022-12-27           0.58  \n",
       "2022-12-28           0.43  \n",
       "2022-12-29           0.33  \n",
       "2022-12-30           0.41  \n",
       "\n",
       "[728 rows x 9 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "huada_df = pd.read_csv('../dataFiles/huada_df.csv',\n",
    "                      index_col = 'date', \n",
    "                      parse_dates = True)\n",
    "huada_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4e41a054",
   "metadata": {},
   "outputs": [],
   "source": [
    "huada_dReturn = huada_df['close'].pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "00b1270f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.064065802566713e-05"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "huada_dReturn.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3fc80316",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.064065802566713e-05"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "huada_dReturn = huada_df['close'].pct_change()\n",
    "huada_dReturnMean = np.mean(huada_dReturn)\n",
    "huada_dReturnMean\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aab8b221",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "日平均收益为0.000040641\n"
     ]
    }
   ],
   "source": [
    "huada_dReturnMean = np.mean(huada_dReturn)\n",
    "print('日平均收益为%.9f' % huada_dReturnMean)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d631f6b4",
   "metadata": {},
   "source": [
    "## 5.1.2 std( )函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8e68171b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = (8, 2)   \n",
    "plt.rcParams['figure.dpi'] = 180"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7cb913c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pingan_df = pd.read_csv('../dataFiles/中国平安.csv',index_col = 'date',parse_dates = True)\n",
    "pingan_dReturn = pingan_df['close'].pct_change()\n",
    "plt.hist (pingan_dReturn,bins = 40,facecolor = '#CDC0B0')\n",
    "plt.axvline (pingan_dReturn.mean(),color = 'black',linestyle = 'dashed',linewidth = 2)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9fcba230",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6HgAAAIByqqkQLElOPvnkJMlNN92UhQsXbjD/u9/9blasWJHevXvnpJNOWm9eQ0NDjjvuuCTJVVddleXLl28wfvr06UnWXg9s0qRJnVs8AAAAADWpy0KwZcuW5fXXX6/8rFmzJsnai9K3nb5ixYr1xn3lK1/JkCFD0tramqOPPjpPPPFEkuTtt9/OVVddlfPPPz9Jcvrpp2f48OEbvO6FF16YhoaGvPLKKzn22GPzhz/8IcnaI8guvPDCfP/730+SnHfeeRk8eHBXbT4AAAAANaTLrgl2wAEHZNGiRRtMv/TSS3PppZdWnp9yyim54YYbKs+bmppy++2358gjj8wzzzyTgw46KAMGDMiqVavyzjvvJFl7GuOVV1650dcdNmxYbrnllhx//PF58MEHM3z48DQ1NWXFihVZvXp1kuRzn/tczj333E7cWgAAAABqWc2dDpkkY8aMybx583L22Wdnn332yTvvvJOGhoYceuihueaaa3LXXXelb9++7Y4/6qij8tRTT+W0007L0KFDs3LlygwaNCif+MQn8tOf/jTXX3996urqunGLAAAAAKimLjsSbGPX89oSu+yyS6644opcccUVWzV+7733ztVXX92hGgAAAAAohy4LwQAAqmnGjBlJkqZ+f5k25dTJVaoGAIBqE4IBAKV08803bzBNCAYAsO2qyWuCAQAAAEBnEoIBAAAAUHpCMAAAAABKTwgGAAAAQOkJwQAAAAAoPSEYAAAAAKUnBAMAAACg9IRgAAAAAJSeEAwAAACA0hOCAQAAAFB6QjAAAAAASk8IBgAAAEDpCcEAAAAAKD0hGAAAAAClJwQDAAAAoPSEYAAAAACU3nbVLgAAqJ4nH5hV7RIAAKBbCMEAgFKaOXNmkmSPQVUtAwCAGuF0SAAAAABKz5FgAAA1pjtOUz1w3MQufw0AgFriSDAAAAAASk8IBgAAAEDpCcEAAAAAKD0hGAAAAAClJwQDAAAAoPTcHRIAKKVJkyZtMO2J+2d2ex0AANQGR4IBAAAAUHpCMAAAAABKTwgGAAAAQOkJwQAAAAAoPSEYAAAAAKUnBAMAAACg9IRgAAAAAJSeEAwAAACA0hOCAQAAAFB6QjAAAAAASk8IBgAAAEDpCcEAAAAAKD0hGAAAAAClJwQDAAAAoPSEYAAAAACUnhAMAAAAgNITggEAAABQettVuwAAgK5wwgknJEma+lW5EAAAaoIQDAAopcmTJydJ9hhU3ToAAKgNTocEAAAAoPSEYAAAAACUnhAMAAAAgNITggEAAABQekIwAAAAAEpPCAYAAABA6W1X7QIAALrCjBkzkiRN/f4ybcqpk6tUDQAA1SYEAwBK6eabb95gmhAMAGDb5XRIAAAAAEpPCAYAAABA6QnBAAAAACg9IRgAAAAApScEAwAAAKD0hGAAAAAAlJ4QDAAAAIDSq8kQ7IYbbkhdXd17/tx7773truO1117LOeeckxEjRqS+vj477LBDxo4dm2uvvTZFUXTj1gAAAABQbdtVu4BN6dWrV/7mb/6m3fl9+/bd6PQnnngiRx55ZJYsWZIkaWxsTHNzc+bMmZM5c+bk1ltvzS9+8Yt2xwMAAABQLjV5JNg6e+yxR1599dV2f8aOHbvBmOXLl+eYY47JkiVLsu++++axxx5Lc3NzWlpa8p3vfCd9+vTJ7Nmzc/bZZ1dhiwAAAACohpoOwbbGZZddlldffTX19fW58847c9BBByVJtt9++3zpS1/KBRdckCS5+uqr89xzz1WzVAAAAAC6SelCsBtvvDFJcuKJJ2bYsGEbzD/zzDPT2NiY1atX58c//nF3lwcAAABAFZQqBJs/f35eeumlJMmECRM2ukxjY2PlNMrZs2d3W20AAAAAVE9Nh2B//vOfM2bMmDQ2Nqa+vj577bVXPvvZz+b+++/f6PJz586tPB41alS7610375lnnunUegEAAACoTXVFURTVLuKv3XDDDTn11FMrzwcPHpyWlpa8/fbblWmnnnpqrr766my33V9ucPntb387Z511VpK1F8gfOHDgRtf/rW99K//yL/+SJGlubk5jY+N71lRXV7dZtc+cOXOzlgMAutakSZM2mOZ9GgCgZ1n3ma4z4quaPBJst912y7//+7/nf/7nf7Jq1aosXbo0ra2teeihh/Lxj388SXL99ddvcIfH5ubmyuP+/fu3u/6289qOAQAAAKCctnvvRbrf+PHjM378+PWm9e7dOx/96Edzzz335NOf/nRmzZqV733veznrrLOyzz77dHlN75U4rjtSbOLEiV1eS1ebNWtWknJsC9Whh+gM+qh7PPnArGqX0K32GFTtCmrHgeP839oc9kV0lB6iM+gjOkoPrVWTIdim9OrVK5dddllmzZqVNWvW5Je//GX+9V//NUkyYMCAynKtra3tng7Z2tpaedx2DABQHutOfRR8AQCQ1OjpkO/l/e9/f3baaackyYsvvliZvttuu1UeL168uN3x6+YNHDhws64HBgAAAEDP1iNDsPa0vSNk2ztF/rV18/bbb78urwkAAACA6uuRIdgLL7yQ119/PUkybNiwyvQRI0bkfe97X5Lk7rvv3ujYlpaWPPjgg0mywXXHAAAAACinmgvB3usC9EVR5Nxzz02y9vpgxxxzzHrzTz755CTJTTfdlIULF24w/rvf/W5WrFiR3r1756STTuqcogEAAACoaTUXgi1atCgf/vCH84Mf/CAvvvhiJRRbs2ZNHnnkkUyYMCE///nPkyRTpkzJiBEj1hv/la98JUOGDElra2uOPvroPPHEE0mSt99+O1dddVXOP//8JMnpp5+e4cOHd+OWAQAAAFAtNXl3yMceeyyPPfZYkqRv374ZMGBAmpub89Zbb1WWOfXUU/Of//mfG4xtamrK7bffniOPPDLPPPNMDjrooAwYMCCrVq3KO++8k2TtaZBXXnll92wMAAAAAFVXcyHYLrvskm9/+9t5+OGH89///d/585//nGXLlqVfv34ZNmxYPvrRj+bzn/98DjnkkHbXMWbMmMybNy/Tp0/P7bffnj/+8Y9paGjIqFGjcsopp+Tzn/98evWquYPgAAC6zZMPzOry1zhw3MQufw0AgM1VcyFYfX19vvzlL+fLX/5yh9azyy675IorrsgVV1zRSZUBAD3JpEmTNpj2xP0zu70OAABqg8OhAAAAACg9IRgAAAAApScEAwAAAKD0hGAAAAAAlJ4QDAAAAIDSE4IBAAAAUHpCMAAAAABKTwgGAAAAQOkJwQAAAAAoPSEYAAAAAKUnBAMAAACg9LardgEAwMY9+cCsapcAAACl4UgwAAAAAEpPCAYAAABA6QnBAAAAACg9IRgAAAAApScEAwAAAKD03B0SACilE044IUnS1K/KhQAAUBOEYABAKU2ePDlJsseg6tYBAEBtcDokAAAAAKUnBAMAAACg9IRgAAAAAJSeEAwAAACA0hOCAQAAAFB6QjAAAAAASm+7ahcAANAVZsyYkSRp6veXaVNOnVylagAAqDYhGABQSjfffPMG04RgAADbLqdDAgAAAFB6QjAAAAAASk8IBgAAAEDpCcEAAAAAKD0hGAAAAAClJwQDAAAAoPSEYAAAAACUnhAMAAAAgNITggEAAABQekIwAAAAAEpPCAYAAABA6QnBAAAAACg9IRgAAAAApScEAwAAAKD0hGAAAAAAlJ4QDAAAAIDSE4IBAAAAUHrbVbsAAICuMHPmzCTJHoOqWgYAADXCkWAAAAAAlJ4QDAAAAIDSE4IBAAAAUHpCMAAAAABKTwgGAAAAQOm5OyQAAF3iyQdmdcvrHDhuYre8DgDQszkSDAAAAIDScyQYAFBKkyZN2mDaE/fP7PY6AACoDUIwANgK3XWaFwAA0DmcDgkAAABA6TkSDIBScYQWAACwMY4EAwAAAKD0HAkGAECP5ghQAGBzOBIMAAAAgNITggEAAABQek6HBKDbbM0pS05zAgAAOoMjwQAAAAAovdKGYM3NzZk2bVpGjx6dxsbGNDU15eCDD87ll1+et99+u9rlAQAAANCNSnk65KJFi3L44Ydn4cKFSZL+/fvnrbfeyuOPP57HH388P/7xj3Pfffdl8ODB1S0UAAAAgG5RuiPBVq9enWOPPTYLFy7Mrrvuml/96ldpaWlJa2trbrrppgwYMCC///3vc9JJJ1W7VAAAAAC6SelCsBtuuCFPP/10kuS2227Lxz/+8SRJr169csIJJ+QHP/hBkuSuu+7KfffdV7U6AQAAAOg+pTsd8kc/+lGS5GMf+1g+8pGPbDD/xBNPzDe+8Y0sWLAgN954Y4444ojuLhGg5rgDI8Cmddd+8sBxE7v8NbpjW7pjOwBgS5XqSLDW1tY89NBDSZIJEyZsdJm6urp88pOfTJLMnj2722oDAAAAoHpKdSTYs88+mzVr1iRJRo0a1e5y6+a9+uqrWbp0aXbYYYduqa+n6eq/EvoLIWXgr+kAdCZH5gJsG3yPqI5ShWAvv/xy5fHuu+/e7nJt57388subFYLV1dVtVg2buxwA0P3GHD6p2iUAAFAlpTodsrm5ufK4f//+7S7Xdl7bMQAAAACUU6mOBOtKRVFUu4Rus+5otm1pm+lceojOoI/oKD1EZ9BHdJQeojPoIzpKD61VqiPBBgwYUHnc2tra7nJt57UdAwAAAEA5lSoE22233SqPFy9e3O5ybee1HQMAAABAOZUqBBs5cmR69Vq7SXPnzm13uXXzhgwZ4s6QAAAAANuAUoVg/fv3zyGHHJIkufvuuze6TFEUueeee5Ik48eP77baAAAAAKieUoVgSXLKKackSX7961/nd7/73Qbzb7311rz44otJkpNPPrlbawMAAACgOkoZgo0ePTpFUeS4447LfffdlyRZs2ZNbr311px22mlJkgkTJuSII46oZqkAAAAAdJO6ooT3x1y4cGE+9rGPZeHChUnWnia5Zs2arFq1KklywAEH5L777svgwYOrWGXtcutUOkoP0Rn0ER2lh+gM+oiO0kN0Bn1ER+mhtUoZgiVJc3NzLrvssvzsZz/LggUL0qtXrwwfPjyTJ0/OmWeeme23377aJQIAAADQTUobggEAAADAOqW7JhgAAAAA/DUhGAAAAAClJwQDAAAAoPSEYAAAAACUnhAMAAAAgNITggEAAABQekIwAAAAAEpPCAYAAABA6QnBSq65uTnTpk3L6NGj09jYmKamphx88MG5/PLL8/bbb3d4/a+99lrOOeecjBgxIvX19dlhhx0yduzYXHvttSmKYrPW8fzzz+ecc87JqFGj0tTUlIaGhuy1116ZNGlSvve973W4RjquJ/RRWxMmTEhdXV3q6upy+OGHd7g+Oq5We+j555/PFVdckWOPPTZ77rln+vbtm4aGhgwfPjxf+MIX8sQTT3S4Nt5bV/ZHR/cvL7zwQqZMmZJhw4alX79+2XnnnXPkkUfmtttu61BddL5a7CP7mJ6lFnuoPT7r1K5a7yPfvWpfrfbQu+++m+uuuy6f+MQnsvPOO6dPnz4ZMGBARo8enX/+53/OCy+80KHauk1BaS1cuLAYOnRokaRIUvTv37/o27dv5fkBBxxQLF26dKvX//jjjxc77rhjZX2NjY3FdtttV3k+fvz4YtWqVZtcx5VXXrleTfX19cWAAQMqz5uamra6PjpHT+ijtq6//vrK2CTFuHHjtro2Oket9tCcOXPW65UkxYABA4rtt9++8rxXr17F+eef35HN5z10ZX90dP9yxx13FP37968sP3DgwKJXr16V56eeemqxZs2ard10OlEt9pF9TM9Siz3UHp91alet95HvXrWvVnto6dKlxd/+7d9u8J7Wdnzfvn2LW265pSOb3y2EYCX17rvvFqNHjy6SFLvuumvxq1/9qiiKoli9enVx0003VXZ2EyZM2Kr1v/HGG8WQIUOKJMW+++5bPPbYY0VRFMVbb71VfOc73yn69OlTJCnOOOOMdtdx+eWXF0mKurq64ktf+lLxv//7v5V5y5YtK2bPnl2cc845W1UfnaMn9FFbr7zySjF48OBi0KBBxciRI30wrAG13EO//vWvi969exeTJk0qbr311uL111+v1Pzoo48Whx56aOVN/dprr93K3wCb0pX90dH9y4svvlg0NDQUSYpDDjmkmD9/flEURdHc3FxMnTq10hvTp0/fyq2ns9RqH9nH9By12kMb47NO7ar1PvLdq/bVcg+dfPLJlfesadOmrfeedv/99xf7779/JVj905/+tJW/ge4hBCupa6+9ttKkv/3tbzeY/5Of/KQy/957793i9Z933nmVJn/xxRc3mP/Nb36zSFL07t278sWhraeeeqryH+1b3/rWFr8+3aPW++iv/f3f/32RpLjmmmuKcePG+WBYA2q5h/74xz8Wzz33XLvrfuutt4oPfOADRZJi77333uLaeG9d2R8d3b989rOfLZIUQ4YMKZYtW7bB/NNPP71ydFhHjmSk42q1j+xjeo5a7aGN8VmndtVyH/nu1TPUag+tWrWqcjTaKaecstH1P//885Xavv/9729Rbd1NCFZSY8eOLZIUH/vYxzY6f82aNcWwYcOKJMXJJ5+8xet/3/veVyRrTwXZmObm5qKxsbFIUkydOnWD+SeeeGKRpPjwhz+8xa9N96n1Pmrr5ptvrnwQXLNmjQ+GNaIn9dDGXHLJJZU3dEFH5+vK/uhIb6xYsaKor68vkhQXXHDBRscvWLCg0hs//OEPt6g2Olet9tHmsI+pDT2lh3zWqW213Ee+e/UMtdpDr7zySuW96tvf/na7r7HDDjsUSYrLLrtsi2rrbi6MX0Ktra156KGHkqy9aObG1NXV5ZOf/GSSZPbs2Vu0/vnz5+ell17a5PobGxszduzYja6/paWlckHhz3/+81v02nSfWu+jtpYsWZIzzzwzffv2zdVXX526urotqoWu0ZN6qD39+vWrPF69evUWj6d9XdkfHe2NOXPmZOXKlZscP3To0IwcOXKLa6Nz1XIfbQ77mOrrKT3ks05tq+U+8t2rZ6jlHtpll13S0NCQJHn88cc3Ov6FF17I0qVLkyQHHXTQZtdWDUKwEnr22WezZs2aJMmoUaPaXW7dvFdffbXSsJtj7ty5G6xjU+t/5pln1pv+6KOP5p133kmSjBs3Lr/+9a9zzDHHZKeddkq/fv2y11575Qtf+ELmzZu32TXR+Wq9j9o666yz8n//9385//zzM3z48M2uga7Vk3qoPffff3+SZNddd82OO+64xeNpX1f2R0d7o+34/fff/z3He7+qnlruo81hH1N9PaWHfNapbbXcR7579Qy13EN1dXWZMmVKkuRHP/pRLrjggixZsiTJ2j/gPPDAA5k4cWKS5B/+4R8ybty4zaqrWoRgJfTyyy9XHu++++7tLtd2Xtsxnb3+N998MytWrKhMf+6555Ks/c/005/+NEcccUTuuOOOrFq1Kn369MmCBQvywx/+MAceeGBuvPHGza6LzlXrfbTOL3/5y/zkJz/JqFGj8tWvfnWzX5+u11N6qD0PP/xwZs6cmST54he/6K/unawr+6OjvbFu/ODBg9O/f//3HL8lfUvnquU+ei/2MbWhJ/SQzzq1r5b7yHevnqGWeyhJLr744px88slJkmnTpmWnnXZKU1NT+vXrl8MPPzwrV67M9OnTc9NNN21WTdUkBCuh5ubmyuNNfXhvO6/tmK5e/7JlyyqPzz///Oy///757W9/mxUrVqS5uTmPPvpoPvShD+Xtt9/OF77whXYPuaRr1XofJcny5ctzxhlnpFevXrnmmmvSp0+fzX59ul5P6KH2/PnPf87kyZOzZs2a7LPPPr50dIGu/Pfr6LrXPd7U2Lbzt6Rv6Vy13EebYh9TO2q9h3zW6RlquY989+oZarmHkrWn71977bW59NJLK/uhN998M++++26StadzLl26NG+99dZm1VRNQrAaccMNN6Surm6rf+6+++5qb8JmW3eYZ1EU6du3b26//fZ85CMfqcw/+OCDc/vtt6d///559913c9FFF1Wr1B5nW+qjJDnnnHOyePHi/NM//VP+7u/+rtrllMK21kMbs2LFinzqU5/KokWLMmDAgNx6661pbGysdllASdjHsCV81qGjfPeiMyxYsCBjxozJueeem+OOOy6PP/54mpub89JLL1W+P0yfPj2HHXbYFh0VXQ1CsBIaMGBA5XFra2u7y7Wd13ZMV6+/7ePjjz8+e+655wZjd9999/zjP/5jkuTee+91sdgqqPU+uvfee3Pdddfl//2//5dvfvObm/26dJ9a76GNaWlpydFHH51HHnkkjY2NufPOO/PBD35ws2ti83Vlf3TW+9SmxradvyV9S+eq5T7aGPuY2lPLPeSzTs9Ry33ku1fPUMs9tHr16kycODFPP/10Tj755MyYMSNjxoxJY2Nj9thjj5xyyim577770rdv3zzxxBOZPn36ZtVVLdtVuwDWmjx5co455pitHt/U1FR5vNtuu1UeL168OB/4wAc2Ombx4sUbHfNe/nr9AwcO3OT6Bw4cuN5fONueh7zuzlobs25eS0tLlixZkp133nmza9xWbUt9dNpppyVJLrnkktTV1W3wF4d1b96rV6+uzKuvr0/v3r03u8Zt0bbUQ39t3ZfT3/zmN2loaMgdd9yRQw89dLPrYct0ZX90tDfWjV+2bFlaW1vbPXVg3fgt6Vs6Vy330V+zj6lNtdxDPuv0HLXcR7579Qy13EOzZ8/O008/nST5yle+stGxI0eOzNFHH52f/exnue222/If//Efm1VbNQjBakTfvn3Tt2/fTlnXyJEj06tXr6xZsyZz585t9zao6+4SMWTIkOywww6bvf62d5SYO3duuzvTdevfb7/91pve9j/0pi4CWxTFZi3HX2xLfbRw4cIkqfzVqj1z5syp/CXj5z//eSZNmrTZNW6LtqUeamvdl9MHHngg/fv3zx133JHDDjtss2thy3Vlf3S0N9qOnzdvXg4++OBNjt/UHSTpWrXcR23Zx9SuWu4hn3V6jlruI9+9eoZa7qG2d4vce++9232dffbZJ8naUydrmdMhS6h///455JBDkqTd6/MURZF77rknSTJ+/PgtWv+IESPyvve9b5Prb2lpyYMPPrjR9b///e/PXnvtlWTTt4J+9tlnk6xNot02vPvVeh9R+3pKD7W0tOSoo47KAw88kIaGhtx55501f2vnMujK/uhobxx66KGpr6/f5PhFixZV3qfsn6qnlvuo7TL2MbWrJ/QQta+W+8h3r56hlnuoV6+/xEaLFi1q93Vee+21JD3gMhEFpXTttdcWSYq6urrikUce2WD+zTffXCQpkhT33nvvFq//vPPOK5IU/fv3LxYsWLDB/OnTpxdJit69exfz58/fYP6FF15YJCn69u270fF/+tOfiv79+xdJihNPPHGL66Nz1Hofbcq4ceOKJMW4ceO2uC46T6330IoVK4rDDjusSFI0NDQUDzzwwBbXwNbryv7oaG989rOfLZIUu+66a/HGG29sMP+MM84okhQDBgwoli5dukW10blquY/sY3qGWu6hTfFZp7bUch/57tUz1GoP3X///ZXXPfPMMze6/ldeeaVoamoqkhSf+tSntqi27iYEK6l33nmnGD16dJGk2H333Sv/SVavXl3ccsstxcCBA4skxYQJEzY6/t///d8rjb6x/yRvvPFGMWTIkCJJsd9++xWPP/54URRF8dZbbxXf+973iu23375IUpxxxhkbXf+KFSuKPffcs0hSjB49unj44Ycr8x577LHiQx/6UJGkqK+vL5555pkO/jbYWrXeR5vig2FtqOUeamlpKQ4//PAiSdHY2Fj85je/6bwNZ7N0pD+6ev/y4osvFg0NDUWSYuzYscVzzz1XFMXa968LLrigqKurK5IU06dP76TfBlurVvvIPqbnqNUeei8+69SWWu4j3716hlrtodWrVxcf/OAHKwHd2WefXSxevLgoiqJYuXJlcddddxX77LNPZf7999/fib+VzicEK7EFCxYUQ4cOrfxn6N+/f9GvX7/K8wMOOKDdv16/13+ioiiKxx9/vNhxxx0ryw0YMKDo06dP5fn48eOLVatWtVvfs88+W+y+++6V5RsbG4vGxsb1nv/yl7/sjF8FHVDrfdQeHwxrR6320I9+9KPKMv369St22WWXTf489NBDnflr4f+3tf3RHfuXO+64o/KX8SRFU1NT0bt378rzz33uc8WaNWs661dBB9RiH9nH9Cy12EPvxWed2lPLfeS7V89Qqz30/PPPF3vttVdl2XU906tXr8rz3r17F9/61rc689fRJVwTrMSGDh2ap556KlOnTs2oUaNSV1eXPn36ZMyYMbnsssvyyCOPZPDgwVu9/jFjxmTevHk5++yzs88+++Sdd95JQ0NDDj300FxzzTW56667NnmB7X333Tfz5s3L1KlTKxdsXL16dUaMGJGzzjorc+fO7dBd6ugctd5H1L5a7aE1a9ZUHq9atSqvvfbaJn/efvvtra6R9nVlf3R0/3LUUUflqaeeymmnnZahQ4dm5cqVGTRoUD7xiU/kpz/9aa6//noXD64RtdhH9jE9Sy32ED1PLfeR7149Q6320N57752nnnoqV155ZQ4//PDstNNOWbVqVfr165d99903U6ZMyZNPPpmzzjqrI5vfLeqKos1tIAAAAACghBwJBgAAAEDpCcEAAAAAKD0hGAAAAAClJwQDAAAAoPSEYAAAAACUnhAMAAAAgNITggEAAABQekIwAAAAAEpPCAYAAABA6QnBAAAAACg9IRgAAAAApScEAwAAAKD0hGAAAAAAlJ4QDAAAAIDSE4IBAAAAUHpCMAAAAABKTwgGAAAAQOkJwQAAAAAoPSEYAAAAAKUnBAMAAACg9IRgAAAAAJTe/wfAH9Uh+uakQAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boc_df = pd.read_csv('../dataFiles/中国银行.csv',index_col = 'date',parse_dates = True)\n",
    "boc_dReturn = boc_df['close'].pct_change()\n",
    "plt.hist (boc_dReturn,bins = 40,facecolor = '#CDC0B0')\n",
    "plt.axvline (boc_dReturn.mean(),color = 'black',linestyle = 'dashed',linewidth = 2)\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "488e436b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "中国平安收益标准差:  0.019682746057661647\n"
     ]
    }
   ],
   "source": [
    "# 计算中国平安标准差\n",
    "pingan_dReturn_sd = np.std(pingan_dReturn)\n",
    "print('中国平安收益标准差: ', pingan_dReturn_sd)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3c4ff4b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "中国银行收益标准差:  0.007975924350195805\n"
     ]
    }
   ],
   "source": [
    "# 计算中国银行标准差\n",
    "boc_dReturn_sd = np.std(boc_dReturn)\n",
    "print('中国银行收益标准差: ', boc_dReturn_sd)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8144010",
   "metadata": {},
   "source": [
    "## 5.1.3 偏度与峰度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bf4022c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "中国平安收益偏度为 0.3972066815984189\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import skew\n",
    "# 输入本地中国平安数据\n",
    "pingan_df = pd.read_csv('../dataFiles/中国平安.csv',\n",
    "                             index_col = 'date', \n",
    "                             parse_dates = True)\n",
    "# 计算中国平安收益率\n",
    "pingan_dReturn = pingan_df['close'].pct_change()\n",
    "# 删除因计算收益率产生的NaN\n",
    "nNaN_pingan_dReturn = pingan_dReturn.dropna()\n",
    "# 计算收益分布的偏度\n",
    "returns_skewness = skew(nNaN_pingan_dReturn)\n",
    "print(\"中国平安收益偏度为\", returns_skewness)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d1fdc146",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "中国平安正超额峰度为 2.0976694279304136\n",
      "中国平安峰度为 5.0976694279304136\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import kurtosis \n",
    "# 计算收益分布的正超额峰度\n",
    "pingan_kurtosis = kurtosis(nNaN_pingan_dReturn)\n",
    "print('中国平安正超额峰度为', pingan_kurtosis)\n",
    "# 计算峰度（正态分布）\n",
    "fourth_moment = pingan_kurtosis + 3\n",
    "print('中国平安峰度为', fourth_moment)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c06d4c2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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lpqYqKSlJKSkpTsdmm1u+vnyUvxbeut7nOiT7mPFw6dFHH5UkrVy50mGdn376SefPn1ejRo2crqywadiwofLnz6/169crMTHRYT3bkzRsY7DnxIkTWrhwoQICAtStWzeH9Tp16qTAwEB98sknTsdm2+uqZs2aTuvhMqvNjzlz5igwMFCdO3d22oe977Mn5yX+x2pzxObnn39W3bp1tXPnTvXt21f/+c9/VKtWLaf9jho1SoGBgXr99ded1uN95LKWLVvK19fX6fdeuvx98vX1VYsWLVy2aftefvfddw7r2Pq7+vvurWPhmNXmiCQlJCSoadOmmjZtmqpWrart27dr0KBBTj8o7tu3T4GBgQ5vBbqyXkJCgsqVK8eqKzdZaY6cP39eBQoUUMGCBXXx4kWHx168eFG7d+9WYGBghiea8T6Sc6w0T+yx/aK9d+/eDn+xtWbNGgUGBuqBBx5w2pbtGqNGjRpO68G+nJgr7uI6JJsM4MLRo0eNv7+/KV68uElISLBbp2nTpkaSWbx4sdvt9u7d20gy7777rt3yTZs2GR8fHxMZGem0nZEjRxpJ5tlnn3Va75NPPjGSzIMPPmjS0tLs1tmxY4cJCAgw+fLlM4cPH3bvRG5yVpsfhw4dMpLMrbfeak6dOmX32PPnz5ty5coZSWbRokUe6ReOWW2OGGPMsWPHTFhYmJFkJk+e7Haf69evN5JM5cqVzcWLF+3WOXbsmClcuLDx9fU1v/76q9tt36iaN29uJJmvvvrKbvnChQuNJNOiRQu32tuyZYuRZKpVq2ZSU1MzlV+6dMlUqVLFSDJbtmyxxLFwzkpzxBhj2rVrZySZRo0ambNnz7p9HmXKlDGSzMaNGx3WsbXdu3dvt9uFteZIw4YNjSQzb948h+2/8sorRpJp3ry5x/qFa1aaJ1fat2+f8fX1NcHBwSYuLs5hvQsXLph8+fKZPHnymP3799utk5qaau677z4jyUyYMMGt80Bmnp4rV1q3bp2RZKKiojKVcR2SPYRXcEu3bt2MJPPQQw+Z06dPp7+enJxsBg8ebCSZyMhIc+nSpQzH/fnnn6ZixYqmcePG5sKFCxnK9u7da4KDg01gYKD58ssvMx1XsmRJI8ksWLDA4biSkpJM0aJFjSSzefNmp+dw7tw5U61aNSPJ9OrVK9MF6dq1a9MvPGfOnOm0LWRktflhG0+9evXMwYMHM5Tt37/fNGrUyEgy7dq1y3SsJ+YlMrPaHLFdtLzwwgtZOo+UlJT0oO2JJ54wJ0+ezFD++++/m7vvvttIMq+++mqW2r5Rbdiwwfj5+ZnChQubDRs2ZChbu3atKVSokPHz88v0gb9t27amTJkyZvXq1ZnabNKkiZFkOnbsaM6fP5/++rlz58yzzz5rJJmHH37Y7ni8dSwcs9IcWbx4sZFkSpYs6fRDpj2fffaZ8fHxMcWKFcs01oSEBNO3b18jyZQvX95hkA/7rDRH1q5dawICAkxwcLD55ptvMpQlJyeb0aNHGz8/P3PLLbeYmJgYj/UL16w0T67Uv39/I8n06NHD5TmMHTvWSDIVK1Y0O3bsyFB28uRJ89RTT6Vf46akpLhsD/blxFyxcRZeGcN1SHYQXsEtFy5cSP/AHxISYh5++GHzyCOPmPDwcCPJlClTxuzbty/TcbbfPEkyP/zwQ6byJUuWmKCgoPQPrq1atTJ16tQxvr6+xsfHx4wYMcLpuGyrqWrWrOnWeRw7dsxUqFDBSDKhoaGmYcOG5tFHHzUVKlQwPj4+JiAgwGWfyMxq8yMlJcW0atXKSDJ58uQxUVFR5oknnjDVq1c3AQEB6cGDow8P2Z2XyMxKcyQ2Nja9zWbNmplnn33W6Z+rfyuXkJBg6tataySZoKAgU69ePfP444+bKlWqGD8/P+Pr62u6d+/OReUVpk6davz8/IwkU6NGDdOqVStTvXp1I8n4+/tn+oXB33//nf49shcy//vvv6Zq1apGkgkLCzPNmzc3zZs3N4ULFzaSzD333ONw5aW3joVzVpkjLVu2NJLMnXfe6fK9oX///pmOf/vtt9PPIzIy0jz22GOmfv36pmDBgkaSueuuu0x0dLTnvnA3EavMEWOMWbBggQkMDDSSTLly5UzLli1N48aNTZEiRdL/Tbv6A7En+oVrVponxly+ZihQoICRZHbu3OnWOfTr189IMj4+PqZ69ermiSeeMFFRUenXO/fff7/dYBRZ4+m5YuMqvOI65NoRXsFtqampZvr06SYqKsqEhoaaoKAgU6lSJTNixAgTHx9v95g//vjDVKxY0TRq1MgkJibarbNr1y7TuXNnU7JkSRMQEGCKFi1qHnnkEaeJto3tDWbu3Llun8elS5fMrFmzTMOGDU1ERIQJCQkx1atXN507dzZ79uxxux1kZMX5sXr1avPoo4+aO+64w+TLl89UrFjRtGrVynz//fcuj81Ov7DPKnPk+++/T7/4cOfPqFGjMrWRlpZmvvzyS/PQQw+Z0qVLm6CgIFO1alXTrl0789tvv137F+kG9uuvv5o2bdqY4sWLm4CAAFOiRAnz9NNPm61bt9qt36ZNG1O6dGmzatUqu+UXLlwwb775prn77rtN/vz5Tf78+U21atXM22+/bZKSkpyOxVvHwjkrzBFboO7On9KlS9ttY8+ePaZz586matWqJjg42JQsWdI0bdrUTJ06lVA7m6wwR2xiYmJMv379TPXq1U3BggVNeHi4ady4sRk3blymlcKe7BeuWWmefPDBB0a6fBtyVmzdutW0bdvWVKxY0eTLl8+ULVvWPPLII6z89zBPzxVjXIdXxnAdcq18jHHx6DUAAAAAAADAS3jaIAAAAAAAACyL8AoAAAAAAACWRXgFAAAAAAAAyyK8AgAAAAAAgGURXgEAAAAAAMCyCK8AAAAAAABgWYRXAAAAAAAAsCzCKwAAAAAAAFgW4RUAAAAAAAAsi/AKAAAAAAAAlkV4BQAAAAAAAMsivAIAAAAAAIBlEV4BAAAAAADAsgivAAAAAAAAYFmEVwAAAAAAALAswisAAAAAAABYFuEVAAAAAAAALIvwCgAAAAAAAJZFeAUAAAAAAADLIrwCAAAAAACAZRFeAQAAAAAAwLL+Dy8gKuvZV4UjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['PingFang HK']  # 显示中文\n",
    "\n",
    "pingan_df = pd.read_csv('../dataFiles/中国平安.csv',\n",
    "                             index_col = 'date', \n",
    "                             parse_dates = True)\n",
    "pingan_dReturn = pingan_df['close'].pct_change()\n",
    "pingan_dReturnMean = np.mean(pingan_dReturn)\n",
    "pingan_dReturnMean\n",
    "mu = pingan_dReturnMean\n",
    "sd = pingan_dReturn_sd\n",
    "norm = np.random.normal(mu, sd, size = 10000)\n",
    "plt.hist(norm, \n",
    " \t\t   bins = 30,\n",
    " \t\t   density = True,\n",
    " \t\t   label = '正态分布')\n",
    "plt.hist(nNaN_pingan_dReturn, \n",
    "           bins = 30, \n",
    " \t\t   alpha = 0.8, \n",
    "           density = True,\n",
    "           facecolor = \"#CDC0B0\",\n",
    "           label = '中国平安收益分布')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63ad6c7b",
   "metadata": {},
   "source": [
    "## 5.1.4 相关性和协方差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "763d106b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>三一重工</th>\n",
       "      <th>海通证券</th>\n",
       "      <th>韦尔股份</th>\n",
       "      <th>上汽集团</th>\n",
       "      <th>隆基绿能</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>三一重工</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.483280</td>\n",
       "      <td>0.420494</td>\n",
       "      <td>0.252100</td>\n",
       "      <td>0.333599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>海通证券</th>\n",
       "      <td>0.483280</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.368538</td>\n",
       "      <td>0.319467</td>\n",
       "      <td>0.396142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韦尔股份</th>\n",
       "      <td>0.420494</td>\n",
       "      <td>0.368538</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.285431</td>\n",
       "      <td>0.382940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上汽集团</th>\n",
       "      <td>0.252100</td>\n",
       "      <td>0.319467</td>\n",
       "      <td>0.285431</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.170475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>隆基绿能</th>\n",
       "      <td>0.333599</td>\n",
       "      <td>0.396142</td>\n",
       "      <td>0.382940</td>\n",
       "      <td>0.170475</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          三一重工      海通证券      韦尔股份      上汽集团      隆基绿能\n",
       "三一重工  1.000000  0.483280  0.420494  0.252100  0.333599\n",
       "海通证券  0.483280  1.000000  0.368538  0.319467  0.396142\n",
       "韦尔股份  0.420494  0.368538  1.000000  0.285431  0.382940\n",
       "上汽集团  0.252100  0.319467  0.285431  1.000000  0.170475\n",
       "隆基绿能  0.333599  0.396142  0.382940  0.170475  1.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入多只股票收盘价\n",
    "stocks_close = pd.read_csv('../dataFiles/sz50_5s.csv',\n",
    "                                 index_col = 'date', \n",
    "                                 parse_dates = True)\n",
    "# 多只股票的月收益\n",
    "stocks_mReturns = stocks_close.resample('M').ffill().pct_change()\n",
    "# 删除缺失值NaN\n",
    "stocks_mReturns = stocks_mReturns.dropna()\n",
    "# 通过corr( )函数计算相关性\n",
    "stocks_mReturns.corr()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3b7c9ef8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>三一重工</th>\n",
       "      <th>海通证券</th>\n",
       "      <th>韦尔股份</th>\n",
       "      <th>上汽集团</th>\n",
       "      <th>隆基绿能</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>三一重工</th>\n",
       "      <td>0.010994</td>\n",
       "      <td>0.004275</td>\n",
       "      <td>0.006692</td>\n",
       "      <td>0.002163</td>\n",
       "      <td>0.005155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>海通证券</th>\n",
       "      <td>0.004275</td>\n",
       "      <td>0.007119</td>\n",
       "      <td>0.004720</td>\n",
       "      <td>0.002206</td>\n",
       "      <td>0.004926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>韦尔股份</th>\n",
       "      <td>0.006692</td>\n",
       "      <td>0.004720</td>\n",
       "      <td>0.023040</td>\n",
       "      <td>0.003545</td>\n",
       "      <td>0.008567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上汽集团</th>\n",
       "      <td>0.002163</td>\n",
       "      <td>0.002206</td>\n",
       "      <td>0.003545</td>\n",
       "      <td>0.006695</td>\n",
       "      <td>0.002056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>隆基绿能</th>\n",
       "      <td>0.005155</td>\n",
       "      <td>0.004926</td>\n",
       "      <td>0.008567</td>\n",
       "      <td>0.002056</td>\n",
       "      <td>0.021722</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          三一重工      海通证券      韦尔股份      上汽集团      隆基绿能\n",
       "三一重工  0.010994  0.004275  0.006692  0.002163  0.005155\n",
       "海通证券  0.004275  0.007119  0.004720  0.002206  0.004926\n",
       "韦尔股份  0.006692  0.004720  0.023040  0.003545  0.008567\n",
       "上汽集团  0.002163  0.002206  0.003545  0.006695  0.002056\n",
       "隆基绿能  0.005155  0.004926  0.008567  0.002056  0.021722"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks_mReturns.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b7faa8e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YsWMHBg4ciLZt2+Yp379/P65fz3+yTF+3ccMGlPUQ6Ctn0N4ZAqEQsdcfSq0Xc+MhTAe0g347Z7zffbHAenF/PSmRcZmN6AQAUu9FBABWzWpDVUcDQR5PkZmaXmC9wBuPYeZcHVXaN0DYvz4y+63SPvsAiqCbBb+mQ+96IyMpFVautaGipY6MpFREPPVHxFP/Qj8Pm9b10WhGL7y9eB9e6/4sdHuqeBq7OUNbRwt3bngiNSWtwHq3rv0Nx4Z10dq9OR7ee1yoezRv2wwTZ47G1fM3sWX1zi/WluhzDk3rQENHE8//eox0Ke/jz248hJ1TDdRr1xC+MlILAICFfXb6lPC37wqso22oCwAFBq6I6MtjYEoGOzs7DB48ON+yHTt2wMXFJd9yLy8vBqaIqFxSNTcCACT7SD89KcU7SKJ+aVIxM4RhRxdkxiUh4gS/gSfptMwNAQDRPsFS60X5hEjUl92vgUS7/IjSMxHrHw6TOrbQMjNArF/RTs7Tq2KGtuvHIsY3FNembC1SH1TxmJpXAgD4er+VWi+n3NSiUqH6t6lqjWWbF8HvtT/mTFj0xdoS/ZeBWfb7cqiMuUjo6+xyAznnIqdXH8GlLX8gKjSywDr12zcEAPg/9pWrT1Jw3MqnEEotMHX//n388MMPRW7/+++/w9XVVeJaq1at4OHhUcyR5eXu7o6LF/P/ht7Hxwfbtm3Lc93T0xMAcPv27XxPI3n+nNtQiKh8UjXLntxlxkpPLp4RnZBd/wsEpsyGdICSijLeH70IUbL8J5nR10nbLDuAlBqbJLVe6sfXsLaZ7MCUkooQGoY6yErPRGZywStVJPo1NyxSYEpFWwMdd06FWCTCxeGrZd6Pvh6VzLO3TsfJOLE3NioWAGBmLn9gSktbCxv2roAoS4SJg39EciFOPi1OW6L86H+ciyTLmIskfny/NTCTby4SFZL3xNTPWde2xXcTeiIjLQOXt5+Rq09ScCImP1cEpRaYSk5OLlZ+paSkvJPNypUrw8FBvv3FhWFlZVVgmaenZ24QKj9Hjx7F0aOFO8KUiKgsKRvqAACyEpKl1stKyH4fVvm45L20CNRUUGlgO4hFIrzfe6lU70UVg7pB9ms4PUH6h+OccvWPr3lZfQqUlJCeKD3YVdh+89N2/Vjo2pri3KAViA+S/iGKvi4GhvoAgKQE6a/DhITsD/P6hnpy971s80JUrmKN0f0mIySw4G1OJd2WKD/aH98/U2Rsp0v5OFfRLuL77ee+adUAw9dOhIqaCo4v2Y/glwHF7pMUAFdMKYRSC0y1aNGiUMk/5bF3794S7U8eQ4YMwe7du/Mdy5AhQ7Bx40aMHz8+T/nEiROxcePGLzFEIqJCyfz47aNQR1NqPeHHU5YyouOl1isu454toGKoi5jrD5DqX7RtUaSYGkzoAgM7C7nrp8Yk4O9FB5Ea83E1n4zT63LKc1Y4yepbLBJBVVv2iXiF6fe/Gs3ohSrtGuD2/H0I/ftFoduTYhg5eTCq2leRu35sdCyWzV+LmOhYAICWjFPudD4enhIbLX1lVY6JM0ej1bfNsWTuKty7Xbgk+8VpSxVfx3HdYWZX8Jf8/5UYE49ji/fmroTSkPGeq/FxrpJYhPfbHKoaaug1eyBa/eCOzIxMHPx5Bzz2Xy5yf6RgmMhcITDHlAwRERG4d+9enut+fn4AAH9//3zLw8PDS31sxSUQCGTWSY/w+wIjIaIvKT08CgCgbCD920eVj99OpodFlep4zIZ1BAC833WhVO9D5Y+1Wx1YNq0ld/34oA/4e9FBJIbHAADUDaSfbJqzoikxPFpm36KMLKREJ0DTWC83qbnMfsNk9/u5qh0awmliF7w6+hee7c7/FCqqGJq2aIxGzZzkrh8S9A7L5q/Fh7DsFXT6BtJXQukb6QMAwsM+yOy73XetMGrKEJw6fBYHdxyTe0zFbUtfh5pudeHQpI7c9SODP+DY4r2I/TgX0ZIxF8lZKRUTXrS5SJX6dhi+eiJMq1og1CcIu6ZvQtBz6TncqILhiimFwMCUDOfPn8f58+cLLF+1ahVWrVr1BUdERFQ8OYEmzerWUutpOFSWqF8adJvVgVYtW6S8fYfYm49K7T5UPp3us6RI7ZI+BoQMq0v/lt6ohpVEfdn9xkDTWA+G1S3x/lH+X8woqSpDv4pZdv2PATJ5OU3sCoGSElS01NFm7Zh862gYZW+dbTKvH9Ljs7ewXGdydIUztMe4IrV7/zHQZFejqtR69g7Z5e/fyQ5MjZoyBEpKStDS0sRvGxfmW8fwY6BrxoKJSIjP3kY4e8LCYrWlr8OqfkVLhB/z8QsDCxlzEcvq2XORmCLMRTpN6oVOk3pBIBDg0tY/cXr1UWSmZxZ+sKTYuGJKITAwVQBDQ0PcvXu3wPLz589j8eLFmD59Or7//vsC61WtKn1iUZbk2WqZEclvFIgqmpgrXrBZOBT6rRtIrWfQugHEWVmIvVp6WzfMRnQCALzfw9xSJL/Qf14iPSEFFi4OUFZXRWYBR41XblUfAOB/5aFc/fpfeQCTb2xRuVW9AgNTFo0doKKljpA7L6SuqsrXx4XKdp0ay6xapd2nf58MTH097t15gMSEJDg3cYS6hhpSU/JPjN+8TVMAwI1Lt2T2mbNC/tsubWTWbfVt89yfZ09YWKy2RNJ4ez5HSkIyqjeuBVV1VaQX8D7+TStHAMCTq/fl7luoLMSQFePg0r05okIisH3SWrx9+LpExk0KiIEphcDAVD5EIhHS09NRp07By1IfP34MALCwsJBaTyQSITMzM9+T+4iIykJa8AfE3ngEg3bOMHBvjJhLebcjG3ZuClVzI8RcvY80GSfcFJVaZVMYtHVCVlIKIo7eKJV7UMWUkZQKn5O38c2Q9qg1sDWe7sgb2DR1rAYzJztEeQcj7F8fufp9eegmnCZ1Rc1+LfFo6/l8T8urN8IdAPB837VCj/t4h3ky6wz8ezV0K1fCoRY/IvYtc659bZKTknHm2AX0H/49ev/QHfu2HclTp65THdRz/gavX73Bw3uPZfbZq63sU7Iv3/8DVpUt8F3T7xHgF1QibYmkSUtKheepW2g92B3NB7TDtZ15d6hUdbRH1QbVEeIdBN/78h+q1XlKb7h0b46QV4FY88MviI+ILcGRk6IRi3kqnyJQKusBlEe3b9+Gtra21MeYMdlL8KdPny6z7sGDB8v4GRERSXq36RTEmVmounIcdBpJnnaq26Q2qi4dDXFmFkI3/iFRZrd5Kup7boZuM/nzSRTEbFhHCIRCRJ74S+YJgUT/9WTnZWQkpcJlZm9UdXeWKDN0sEL7zRMhUFLCgw2n87RtOq8ffri3DnZdm0hcT/4QC++jt6BtZgj3bZOhpvfpgAAlZSFcZveBTev6iPIOhv+VB6XzxOirt3/7ESQnJWPy7LFo27GlRJl9zWpYte0XKCkpYfvaPXnazlgwCdcenkHH7u2/zGCJiuHarvNITUpF9x/7w/HbRhJlljUqY9SGqVBSUsKFTSfztO01ZxCW/bMFjbo0k7hepb4d3Md0RWpiCtYP+41BKcpeMVWUB31RXMaTj+rVq2PrVunL5j09PbF371706dMHrVq1klq3SZMmUsuJiL60hH+9ETBvB2x/GYHafy5B4uM3SAuJgJq1CbTr2UGUkYm3s7Yh8bNvKNWrmMO4mxsAoFK/toj/+3mR76+kqQ6Tvq0BAOG7LxbvydBXKT7gPa5O2ox2G8bB/fcpiPIORqxfGDQr6cO0gR0EAuD+mlN4c0ZyW75QXQV1h7tDSVmIbwa3w5vTnhLldxbuh56tKSq3rItBd9ch7P5riDIyUaleVWiZGiAu8AMuDl8NcRYnrVQ6ggJC8NO4BVi+5X9Yt3sZXr96g4A3QTAxNUJdpzoQCATYvHIHLv55VaKdmroaBo3qA2VlZfQf1gsX/mCCfSrfIgLDsWPKOoxcNxnjtv2IEO8gvH/7DnqV9FHVsTogAM6uO477Z/+RaKeipoq2w76DUFmIVj+4498zf+eWNe7qBqGyEHHxSej+Y3+ZY9g1bUOJPy8qZ5j8XCGUaGCqb9++ePLkSUl2CQCoV68ejhzJu5S5tJibm2P06NFS66irq2Pv3r1wc3OTWfe/hg4dij179shdf968eZg3T/by/06dOuHs2bOFGgsRfb3e77uMxKd+MB/dBbqNakKzpg0youIR+ccthG09g6RnkjnmUv3DEHn6DrQd7fHhyPVi3dukTyso62oh7vZTpLwOLlZf9PUKuPIQxzv+jPqjv4O1Wx3YtnVEWlwSAq8/wtOdlxH6z8s8bbJSM/B012XYdXbB831X85anZeBM/6Wo1b8VHL53g6mjHYSqQiQER+LloZt4vP0iMhJTvsTTo6/YzUu30LvdYAwZNwBNmzdCy/auiI9LwF9X/8aB34/g3p28K/bSUtNw4PejcO/aDod3nyiDURMV3pOrXvil80y0H9kFtVzrom4bJyTHJ+HpjQe4vusCvD3zfgmWkZaO67svoGGnpri577JEmU3dagAAQwtjNOnRPE/b/2Jg6ivA1U8KQSCWJwO2nJo0aSI1YXhRubi4wNPTU3bFEnD58mVcuiQ7Ce/Lly9x5coVtGzZEvXr15dZ38XFBX369AEAzJkzB3/88YeMFoXXsmVLbNmypUT7ZPJzqgge1J1R1kMgKhEPhZqyKxGVc5vSfct6CEQloqlG5bIeAlGx/R5wvKyHUKpSrhXtABGNtvmfnkulo0RXTH2p4FFpunv3LtauXSt3fQ8PD3h4eMisN2LEiNzA1JIlS7BkSdGOyCYiIiIiIiIiqiiYY+o/FixYgAULFpT1MIiIiIiIiIioOLiVTyEwMEVEREREREREFQ+TnysEBqaIiIiIiIiIqOLhiimFwMAUEREREREREVU8DEwpBAamiIiIiIiIiKji4VY+hcDAFBERERERERFVPFwxpRAYmCIiIiIiIiKiiocrphQCA1NEREREREREVPFwxZRCYGCKiIiIiIiIiCoerphSCAxMEREREREREVHFwxVTCoGBKSIiIiIiIiKqeBiYUggMTBERERERERFRxSMWl/UISA5KZT0AIiIiIiIiIiL6OnHFFBERERERERFVPNzKpxAYmCIiIiIiIiKiioeBKYXAwBQRERERERERVTxiBqYUAQNTRERERERERFTxcMWUQmBgioiIiIiIiIgqHp7KpxAYmCIiIiIiIiKiiocrphQCA1Mk1YO6M8p6CETF5vR0ZVkPgahErHeaXtZDICo2OzWTsh4CUYmoJlYr6yEQkSwMTCkEBqaIiIiIiIiIqOJh8nOFwMAUEREREREREVU4YhFzTCkCBqaIiIiIiIiIqOLhVj6FwMAUEREREREREVU83MqnEBiYIiIiIiIiIqKKh1v5FIJSWQ+AiIiIiIiIiIi+TlwxRUREREREREQVD3NMKQQGpoiIiIiIiIio4mFgSiEwMEVEREREREREFY+YOaYUAQNTRERERERERFTxcMWUQmBgioiIiIiIiIgqHp7KpxAYmCIiIiIiIiKiikfMFVOKQKmkOwwODkZMTEyB5T///DPq16+PW7duFfkeWVlZRW5bHt2+fRv/+9//8lx///49HB0dsWjRojxlv/zyC1auXAkx98wSERERERER5SUSF+1BX1SJrpgKCAiAm5sblJWVcfToUTRq1EiiPC4uDuvXrwcA1K9fv0j3OHDgAJYsWYKzZ8+iWrVqudcDAwOxZ8+eQvc3ZcoU6Onp5bl+/vx5nD17Fhs2bICKikqRxiqvv/76CwsWLECtWrXQq1ev3Ou3bt3C48eP0a1bN4n6z549w8KFC+Hu7o4ZM2aU6tiIiIiIiIiIFJGYOaYUQokGpqysrNC9e3ds2LABrq6u2LdvH/r27ZtbvmnTJsTHx0NTUxONGzeWu999+/ahYcOGAIDU1FT4+vqiSZMmOH/+fO71gIAALFy4sFDjVVJSwsiRI/MEpsRiMTZu3IhLly7B19cXJ0+ehL6+PgBg27ZtGDNmTKHuAwAjRozA77//nm/ZtGnTsHnzZsyZMwddu3bNDYTlrCpr0aKFRP2pU6dCJBJhwYIFhR4HERERERER0VeBq58UQokGppSVlbF+/Xp88803GDNmDAYMGID09HT88MMPSE5Oxtq1awEAycnJ8Pb2lrvf5OTk3J9HjBgBCwsL9O7dG+3bt4eHhwfq1auHFi1aSGxrCw0Nxd9//43evXtL9PXo0SMMHToUb9++xaFDh2BhYZHnfgKBAGfPnsX48eOxfft2tGzZEjdu3IChoSH09fXh4OCQ7zhjY2MRHh6OSpUqwdDQUKLMzMyswOenqamJhQsXYvTo0bh06RI6d+4MIHuLn6qqqkQQ7/Hjx7h58yYmT56cG5QjIiIiIiIiov9gjimFUCrJz0eOHAlVVVWMHz8etWrVAgBMmjQJERERWLx4MebNm1es/jt27IizZ8+iQ4cO6Nu3L168eAElJcl0WXPmzMG+fftw9OhRbN26FXp6evjf//6HZcuWoVq1arh7927u2PKjrKyMbdu2wcLCAgsXLkS7du1w48YN9OnTB3369Mm3zc6dOzFixAjMmTMHkydPlvk8Zs2ahaioKABAZmYmGjdujDNnzuDMmTMQi8V49uwZdHR0MGnSJIl2DRo0wIcPHzBy5EgAgJGREZYuXSrzfkRERERERERfDa6YUgildirf4MGD0bZtW1haWmLfvn3YuXMnqlSpUmI5kVq1aoVjx47B3Nw8T1AKAPbs2YNGjRph1qxZqFOnDoyMjODn54fZs2dj7ty5UFNTk+s+CxYsQHJyMl6+fAkNDY0SGXuOo0ePIiAgQOLavXv3JP4cFxeHHTt25Gnr5eWV+7OtrS0DU1+YdoPqMB/VGdrODlAx1EFGdAIS7r1A2PZzSHrypkh9mg3viMrzhyDpiR9edJmde73qmgmo1Ke13P08bDga6aERRRoDUVGEhX/A5DmL8dLnDbat/gXNGjuV9ZDoK2dpb4WOI7qgjms96BnrIyk+EW+fvMHlPRfw/M6TIvfb+LumaNa1OayqW0O/kgHeB4Yj6FUAzmw5hVDfkEL15dazFcauzv7i6Qf775GZnlnkcVHFZG1vja4juqGemyP0jfWRGJ+IN098cW7PWTy5/bjI/Tbr5IrmXVugcnUbGFQyQHhgOAJe+ePk5uMI9g0usJ2JpQl6TeiNqrWrwaqaFbKyshDm/w7/XPwH53efRXpaepHHRIrNyN4CjUZ0hK1rHWgZ6yI1PhlhT97iwZ4rCLjzvEh9CpQEqN+/Ner0dIVhVXMIVZQRFxKB15e88O/vF5CWkCK1vXVjBzgP+xYWjnZQ19NC0odYxAS8x5MjHvC5dB/irIJX0RSnLZVDzDGlEATiUj7W7eXLl2jYsCHS0tLQtWtXVKpUqVDtV61aBU1NTbnqXrt2DX5+fhLX/P39sWbNGqSnp6Nfv3558jXVqlULbm5uMvvOzMyEsrL0OF7Oiqm1a9fKtWKqIGlpaahWrRpSUlIQFBQELS2tIvdVXHctepTZvcsr08HusF08HAJlIRIfv0FaSATUKleCdt1qEGVkImDO7/hw8Krc/Qn1tWG7cChMercCACR4+UgEpioNaAedxjWl9qGkqgKjLs0gzszCg3pDkRmTWLQnV0E5PV1Z1kOosO7c9cKcxSsRHRsHAAxMlbLBTtPLegjlnnP7Rhi/fhrUNNQQ7BOEsLeh0DPRh71jdSgJhTi17hhOrD5cqD7VtdQxZvVkNHJ3QWpSCgJfBSA+Mg6mNmaoXNMWWZlZOL7qEM5sPiVXf1Xr2mH+8V+gqp79JdnXFphKEmeU9RDKvcbtXTB9wwyoaagj0CcQoX4hMKhkgOqONSAUCnF07WEcWn2wUH1qaGlg8uqpaNKhKVKSUhDwyh9xkXEwszGDbc0qyMrMwsFVB3By0/E8bd0HdsDQecOhrqmOd/7vEPImGJo6mrCtWQXaetoIC3iHn/vNRcRX9sVYE4F+WQ+hzNm3d0KX9eOgoqGGCJ9gRL8Ng5aJPiwc7aAkVMLf6/7A7dUnC9WnUE0FvffMgE3T2khLTEGoly+yMjJhVrcKdEwNEBv8AYf7/Ya44Pxfb80mdYPr1OzPMFFvwxDlG5o9pvrVoKQshN/Nxzg5Yg1EmXlPei9OW0U1K/BAWQ+hVCUt7FekdloLCzdXoOIp0RVTcXFxmD59OmbPnp17Yp6xsTEqVaqE2bNn48SJEzh1Sr5JW441a9bk/jx06NB8T97z9vZGjRo1sHPnThw5cqTAvg4fPozDhyVfYGPGjJErMPXfoNTbt28REREBR0dHqKqqymxfGL///jtCQ0Mxb968Mg1KUV46jRxgu3g4MuOT8XrYUiT8+yq3TLdJbVTfORNVfhuF5NfBSLwvPY+amq0ZLCf1hHFXVyhpqCHu9lPoudXNU+/DwasyA11G3ZvDqEszxFz1YlCKvojjpy/g4PEzeOMfCCsLM2hqaiDkXXhZD4u+cqY2Zhi3dgoEAmDNmGW4f/Fubpl1jcr4cfc89JjcG6G+wfA8e0fufvvN/gGN3F3w7PZjbJq8FvFRcbll9k41MG3bLPT+cQCCXgXg8c2HUvvSM9HH1O0zkRibCEMz+VZv09fFzMYcU9dNBwQCLB29BJ4X/8kts6lhg5/3LECfKf0Q7BuM22dvyd3vD7OHoEmHpnh8+xFWT1qJuM9exw5ODpi9fS4G/jgIAS/98eDmp5X5dZvVw6jFY5AUl4hVE1fg36ufVvera6rjh1lD8N2QTpi9fS6mfTeleE+eFIq+jSk6rx0LCAT4Y8w6+Fy8n1tmUsMKvXbPQLPJ3RHpG4pXZ+9K6UlS2wWDYNO0Nt7+9RSnJ2xEWnx2vmElFSGaT+8Fl7Gd0XPHNOzuODfP6qXa3ZvBbXovxL+LwtEfliHK911umY65IXrumIZqreqj2aRueQJmxWlL5Ri38imEvHvgimHLli3YuXMnGjRogBMnTgAAKlWqhIcPH2LUqFGIiYmBoaEhxGIxxGIxYmNjsWzZMqSmpuZey3l06dIFAoEA6urquf03adIEAwcOxMCBA9G/f38oKytDW1s7T6JxPz8/hIeH5z5MTEzQtGlTiWtPnz7N9zn069cPrVq1knj8N5gFAIsXL4aLiwvCwsJK8DeYvVpq6dKlUFdXx8SJE0u0byo+i/E9IFAWwv+nLRJBKQCI93wB/9nbIFAWwnJCd5l9GbRxQqW+bZAZnwyfIb8hZM2xIo/LfMR3AIDwXeeL3AdRYazZsht+AUFwb9Mcx3ZtgLlp4VbDEpWGDsM7Q11LA8dWHpIISgFAsE8QNkxYBZFIhK4TesndZ7V69mgz4FvERcRi7ZjlEkEpAPB94IO9C3dASUkJ7Qd3lNqXUEUZU7b+BB0DXawZvUz+J0ZflS7Du0JDSwMHVxyQCEoBQKBPIFaMXw6RSIReE3sX0ENe9vWqw31gB8RGxGDp6CUSQSkA8H7gjd8XboeSkhI6Du4kUTbm13EQCoXYNHOjRFAKAFKTU7F9/lbcveyJat/YwbFFg0I+W1JkDYd/C1UtddxeeVwiKAUAET4hOD1hI8QiEZpM6Cp3n1qV9FG3d3MkvI/Bn2PX5walAECUkQWPpUfx1uMpKjlYo3r7/6zQFgjgOqUH0pNScfD7xRKBJQBICIvGualbAQB1erkBAkHJtKXyTSwq2oO+qBINTM2aNQuHDx+GQCBA7969sWTJEgCAgYEBACA6OhpGRka59bdu3YqZM2fC2dkZr15JfshPTk7Ok9Np1KhR2L9/P/bv34/69esjMzMTW7ZsgYmJiUQ9U1NTiYdQKISqqqrEtYK2FBoYGMDY2BjGxsYQi8Xw8PBAUFBQsX83n/vrr78gEAjyfairqyM0NBSpqakwNTUtsF5+j7/++qtEx0mS1KwrQb+1I9JCIxB9If9vfaJO/430sCjot24ANSuTfOvkSHkTgtejV+JRw1GIuXJfal1ptJ2qQ9vRHsk+QYj/u2j7+IkKa2Dvbjh/ZAdW/m82dHW0y3o4RFDTVIdrj5ZIT03Htf2X8q3j+9AHbx69RmUHG9RoKH2LdI5aTb+BkpISPI5eQ0pi/jlNvP99CQCo8o2d1L6GLBqBGs41sXPOVvg99pXr/vR1UddUR6uerZGemo6L+y/kW8fnoTdeP/SBrYMtajWqLVe/dZvVhZKSEq4evVrg6/jFvew5hF3dT69jbT1tWFa1RGJcIu5e9iyw/yuHLgMAuo/pKdd4SPGpaKqhTg83ZKam4+H+6/nWeffwDUIfvUElB2tYNawhV7/1+7WCUEUZTw7fRHpSar51/t2R/W/DcVBbievVv3WCga0pnhy+ibiQyHzbRr4OwVKbgdjSdArwWUab4rSlck4kLtqDvqgST37et29fODo6okOHDtiyZQuGDRsGMzMzAEBMTAxq1Pj0pjRz5kzUrFkTQ4cORZMmTXDr1i3UrZu9lSm/wFQOLy8vzJ07F0OGDMHAgQPzlC9ZskRie11iYiICAgKwaNEiiWv52bx5c+7Pf/31F1q2bCn/k//s/lu3bpW4tm/fPjRs2BBAduAsv3Hfvn0bgYGBqF27NhwdHfOUi8VinDx5EmKxGD179syT9N3U1LTQYyX5GbR3hkAoROx16ds0Ym48hOmAdtBv54z3uy8WWC/ur6In4P2c2Yjsbzal3YuopI0bNqCsh0AkoXbTb6Cpo4knHg+RnlpwEuZHNx6gupMDnNo3gs/9VwXWy2FV3RoA8O7tuwLr6BjoAABSEpMLrNN24LdoM+BbXNhxBrdP3pR5X/o61W1WF5o6mnjo8QDpqWkF1vO6cR8OzjXRqF1jvPz3hcx+ratXBgCEvi04Sb+ugS4AydexmU32HD4yLP8P6jki32Xn+rGvay9zLFQx2DStDTUdDbz1eIpMKe+5fjcew8qpOuzbN0DIfR+Z/dp/XAX11qPgeXLwPW+kJ6XCtlltqGqp5waw6vbOziX89Fj2l/UGtqZw6NQYuhbGSPoQi4jXwXjr8RQZyXn/bRWnLZVv4nKW/Pzu3btYs2YN/v77b0RGRsLY2BjNmzfH1KlTc+MF8qpcuTKCgws+tCLHjBkzsGLFiqIO+YsolVP5atSoAU9PT6SmpuYGpcRiMeLi4iRWTAFAly5dcPfuXezZswd16tTJvV5QYCo0NBTdunVDtWrVsHHjxnzvn7NS63OJiYlYuHBhMZ6V/D58+IAPHz5IXEtO/vSfvIODA/bv3y9RvmnTJhw6dAiOjo64ceMG9PX18+3b1dUVY8eOhaWlJZYvX17iY6eCqZpnv3aTfaSvoEvxDpKoX5pUzAxh2NEFmXFJiDjBFXNE9PUy/PieG/xa+nt0yMf3cCMz+d6jc5KaR0pJ6uz8bWMAgN/j1/mW13CuiR8WDMez249x8Ne9ct2Xvk5GZsYAsrfsSZNTbmxuLFe/h1YewMnNJxAR8qHAOo2/bQIAeP3o0+v4nX92QNbc1hxCZSGyCkj4XKV2VQCApo4mNHU0kZxQcJCWKgYd8+xUKhGvpX8ojvTJDobqmBlKrZfbr1n2TpsIn4KDqFnpmYgJCIdpbVtomxkg2i87tYq+TfaOmLjQKLSa0w+NRnaA4D9f5McGf8DFmTsR+LdkQLc4bamcK0ernzZv3oxJkyYhKysLzs7OcHFxgb+/Pw4fPozjx49j06ZNGDVqlFx9iUQihIWFQU9PD507d5Zat0GD8r/NulQCU8Cn1TudO3fGuXPncq+fP38eggL25OYXUMqpKxaLkZKSgk6dOiE0NBS//vorPDw8AAB169aFtbV1bpvExESJpOHm5uZwcHDAzZufvqF8//59btCspBX2VL45c+bgt99+Q+3atXHlypUCg1JAdrL2hw8fYsWKFUhMTMT69etlnhZIJUP144eYzFjpycUzohOy63+BwJTZkA5QUlHG+6MXIUrOf7kzEdHXwPDje3SSjPfohJj47PpyfqCPDJF+yphN7SroNuF7ZKRl4Ny2P/OOy9wIU7b+hKiwKKwfv6rcfXNL5UtOwDQxNkFqvfiPr2MjOecaH6QEpIDswFLviX2QkZaBP7Z9SuqcnJCMt8/9ULVONfSd0g8HV+Y9vUtDWwP9pg2ASCSCkpISTCwrIdA7QK5xkeLKCSClxiZJrZcck/1azglkSaOkIoSmoQ6y0jNlrkxK/jjf1jEzzA1M6VoYISU2EY1HdYTjwDa48ethvLn+CEkfYmFYzQINh3+L2t2aoffeH7G3y3x8ePnpi4zitKVyrpwEpu7cuYNJkyZBT08Pf/75p8QBbB4eHujevTvGjRuH2rVro1mzZjL7+/DhAzIzM1G/fv08i14UUalHNKysrODg4ICUlBQEBgbm5m+SxdfXFyoqKrC1tc29FhQUhMePHwMA5s6dm3t9586dGDZsWEkPvdT5+flh2rRpOHPmDOzt7XHt2jW5fjfbtm2DsrIytmzZgsePH2PXrl1wcHD4AiP+uikbZm/VyJLxLWBWQvZ/0CqGuqU6HoGaCioNbAexSIT3e/PPp0JE9LWQZzsdACR/TKSbU7846rd2wvi1U6CipoJDS/Yi8GWARLmKmgqmbZ8FVQ01/Np/AZLieGoqSafzce6QXEAeqBw5r+Oc7XfF4dTaGdPWzYCKmgr2/LoL/i/9Jco3zlyPZX+sRO9JfWFiVQnndp1BqF8oNLQ1UbtRbQz4aRACvQOQGJsAu7r2ECoLiz0mKv80Pr6Hpsl4rabFZ5dryvGeq2GgA4GSEtISpQe7ACAt4WO/H+fn6vraUNVUh0g1C41GdcSBXovx/nlAbv3wp29xdvIWxIdGocn4LuiwbAT2dp5f7LakAMpJIvOlS5ciKysL27dvlwhKAUDLli2xZcsW9OvXD0uXLsXZs2dl9hcaGgogeztfRVBqgan09HScOXMGa9euhZqaGk6fPo1u3bphyZIlGDlyJADg+vXruHfvHmbPnp1nFZWenh7q1auHW7c+HYNrZ2eHyMhPe9w3bNiARYsWSeStAgBt7byJeMPDwwtcqfWlxcTE4JdffsHGjRuRnp6OAQMG4MyZMzA3Ny9UP5MmTcLWrVtRv359TJo0CTNnzsyzVVIaeX4fnuayT5f7WmR+/GZGqKMptZ5QJ3u1XkZ0fKmOx7hnC6gY6iLm+gOk+pfs6ZBERGWl6/iesLCzkrt+QnQ8DizejYSP38praEt/j9bUzS7PqV8Uahpq6DdnMNr/0AGZGZnY/fN2XN2XN8/fiKXjYFunKtaOXoYQGVsMqWLpNf57WNlby674UUJ0PHb+bwcSPs4dNLXzz7OaI+d1nLNyqijUNNQwZM5QdBzcCZkZmdg2bwsu7Mt7uq/fMz/8OmwxxiwZh1Y9WqNVj9YS5f9c+Bvrp6/FhmubAEDqdkEqf5qM7wIjOwu566dEJ+D64oNI+fgeqibjtaqmm12eLMd7bkpMAsQikcw+AUBN52O/H+fnOatRlZSFeHTohkRg6XN31v2B+v1bwbxuVWibGiDxfUyx2pICKAcrpvz9/XHx4kVYW1ujZ8/8D4no06cPpk+fjgsXLiAgIEBigU5+QkKyt7va2NiU9HDLRKkFpg4fPowhQ4Zg48aNGD9+PF6+zD6x5vMg0oYNG3D69Gk8fPgQBw8ehJqaWm5ZYmIidHQkI+tCoVAi8HLnzh0oKyvn2TM5Z84cieTnK1euhLGxMYYMGSLR/8qVK0vkuRZWhw4dcO/ePXzzzTfYtGkT3NzcMHbs2NyE7M+ePcOTJ0/g7u6e7wqqu3fv4s2bN5g3bx4GDx6M4cOHY8WKFXjw4AGuX8//VAwqvvTwKACAsoxvfFQ+fnOTHhZVquMxG5Z9LPn7Xfmf2kNEpIjquNVD7SbfyF3/Q/B7HFi8G9Ef36O1ZbxH63xcYRItI5lzQarVt8e4NVNgXtUCwT5B2DptHfyfv81Tr+OILnDr0RLHVx2C15V/i3QvUlz13RzxTdO6ctd/HxSOnf/bgaiPr2MdGSuhclZKRRVxrlG9fnVMWTsdllUtEegTiHXTVsPvmV+B9R/deoiJbcbBuU1DWNtbw9jCBNHvo3D/2n28eeoLgUAAg0qGSElKQYKMbYhUvti61YFNk1py148N/oDriw8iITw7KKNhIP1k3pyVUglh0TL7FmVkITk6AVrGehJJzfPt9+N8OyE8u9+0+GSkJaRATUcDoV755/sDgKy0DER4h6Byk5owqWGFxPcxxWpL5Z+4HASmzp49C5FIhI4dOxZYRyAQoGPHjtixYwfOnTuHCRMmSO3z8xVToaGh+N///gcvLy/4+PigcuXKqFOnDmbOnAknJ6cSfS6lpdQCU6tXr4aRkVFuMOjVq+yTbz4PTJ08eRKTJk3C5s2bkZiYiD///BPq6upISkqCSCTKE5j6XGJiIm7fvo369evnSZI+Z84ciRxTW7duha2tLRYsWJB77f3794UOTEVHR0NdXR2amtK/jZVl3bp1uHv3LiZMmAChMHu585YtW3LL3dzcYGBggD///FMiWJejf//+ePPmDXR0dNCgQQN4eXlh27Zt6NatW6HGIZbjmNO7Fj0K1WdFlhNo0qwu/RtQDYfKEvVLg26zOtCqZYuUt+8Qe/NRqd2HiOhL+7Vv0bZHRH98z5W1SsWqRvZ7dE4AoDC6T+qNHpN7QyAAzmw5hROrDyMzPTPfut0m9kJGWgYsqlli7BrpeSdHr5wIUZYod/UXKb55fecUqV1UeHbAtHJ16VszbGpkf0Mu67S8/PSZ3Bd9JvcDBMDJLSdwaNWBAl/Hn0tPS8c/F/7Ot6xK7apQVlFGEFcGKpzDffPm+JVHTqDJ2F76ClfjGtnlOQEkmf2Gx0DLWA/G1S3x7lH+wVKhqjIMbLNzBSeGfwoOxYdFwUTHSmZ+qsz0DACAQPgpuXlx2lI5V8TAlDy7i+T5PA18Wt30+WFv+ckpz6kvTU5gytPTEz/99BPU1dXh5OSESpUq4fnz5zh+/DhOnTqFhQsXYt68eXKNsyyVSmDq2rVrePr0KRYsWJAbIHr58iX09PRyk6ID2SugNm3aBGVlZYSHh0NFRQUAEBcXByB7O19Brl+/jvT0dHTp0qVIY8xZnSSviIgItG3bFqtWrULbtm2LdM8cjRs3RuPGjfMt8/Lywp07dzBmzJh8g1IAEBUVBQ0NDairqwPI/j2OGzeuWGMi2WKueMFm4VDot5Z+qoFB6wYQZ2Uh9qpXqY3FbEQnAMD7PcwtRUQEAC89nyE5IRk1XWpDVV0V6QUcX16/VfZ7+INCrGISKgsxeuVEuHZvgYiQD9g4cTV8H0o/9lwgEEBFTQXNurWQ2X+zrs0BfFr9RV+vZ/9kv45ru9SBqroa0lPz/5Ds1NoZAPDv1Xty9y1UFmLSyilo2aMVPoR8wMoJy+Hz0FtmuyFzh0HfRB8Hlu9H5Lv8DwP4tr87AOD6satyj4cUW5DnS6QlpMDaxQHK6qrILOA9t1qr+gAA3ysP5erX98oDmNWxRdWW9QoMTFk3coCqljoC/n4hsaoq/MlbmFS3gnF1K/heLfh+xtWzg2WfJzAvTlsq58rBoSM5QSRDQ+mHAOTslpInMJVT58iRI1i9ejVGjBghcSjatm3bMGXKFPz8889wc3NDixay5yNlqVQCU0uXLoWmpmbu8jOxWAxvb2+oq6vjxx9/zDsIZWVYWFhg1qxZALJzMAHAgwcP8OOPP8LFxUViL2ZkZGTuCqOuXbsWamxz585FVFQUnj59CiA7Obss7969Q4sWLfDq1SsEBko/vrc4UlJSMHjwYKirq2P69OkF1gsODi50PioqvrTgD4i98QgG7Zxh4N4YMZfyTgYNOzeFqrkRYq7eR5qMk5yKSq2yKQzaOiErKQURR2+Uyj2IiBRNalIq7pzyQPvBHdFmwLe4uDNv4lA7x+qwb1ADQd6B8Ln/Su6+e07tC9fuLRD0KgC/DVqEuIhYmW1G1h0ks86hwD8AAD/Yfy/XihWq+FKSUnDz5A18N6QT3Ae448zO03nqVHesgRoNHBDgHYCX/8p/bH2/qf3RskcrBLzyx4KBPyNWjtcxkH3qXqserRH6JgTHNx7LU25lZ4UW3VogMS4R149dk3s8pNjSk1Lx/NRtOA1uD8cBrXF/Z94vSy0cq8GygR0+eAcj5L70YH6Ox4dvounErqjXtyXubTuf7wqmhiOyA6GP9ku+3v79/QK++b45GvzQFg/2XkV6PonZa3dvBl1zQ0T6hkpsxStOWyrnirhiSt7VUPLIyZOtqyt9m3bOwpzP82oXxMDAAC4uLpgwYQIGDBiQp3z06NGIiYnB7NmzMXny5NxD5MqrEl+DeO3aNVy/fh0jRozIjfgFBQUhKSkJUVFRWLlyZZ7H6tWrsXbt2tw/79y5E0B2YGrlypW4du3Tm87bt2/h5OSEy5cvAwDGjRsnM6KopqaWm3MqIyMDly9fxvv379GjRw+MGTOmwHaij9HV9evXw9vbG8uWLcPw4cOL/suRIiIiAl27dsXLly8xd+5c2NnZ5VsvJSUFvr6+MpOhUel4t+kUxJlZqLpyHHQaSZ6EqNukNqouHQ1xZhZCN/4hUWa3eSrqe26GbjPpyzflYTasIwRCISJP/CXzhEAioq/JxZ1nkZqUgj4/DUBDdxeJMusalTFx43QoKSnh9MYTedr2nzsYGzx/R5MurhLXq9W3R+cx3ZGSmILlQ3+RKyhFVBxndp5GSlIKBs78AS7uTSTKbGrY4MdNM6GkpIQTG/IGiYbMHYadd3fDrUtzievV61dHj7G9kJKYjMVDFskdlAKA60evIjMjE70n9UXDto0kyqztrfG/Q79CQ1sTf2w9iZQk6Se0UcVyf+dlpCelovlPvVHd3VmizKSGFbpunAiBkhI8N+YNsLaa2w/jPNehZhfJ13jSh1g8PXYLOmaG6L5lMtR0P6VQUVIWouWsPqjWqj4+eAfj9ZUHEm0jfELge/UhdMwM8f2u6dAxl1ydUqNDQ7RdOAiizCxc+HF7ibUlkiUnLhIfL/3AipydY/nlmf6vtWvXwtPTM9+gVI5p06ZBRUUFT58+RXJy+f7cWKIrpsRiMWbPng1lZWVMmzYt97qNjY3cEUeRSIRp06Zh3bp1OHr0KHr37p1blpaWhp49eyIkJAQbNmyAmpoapk6dCkdHR/z666/Yt28fDh8+nKfPgICA3J+XL1+O5cuXyzWWK1euAAA0NDSwf//+AjPoF4dYLMaff/6J8ePHIywsDFOnTsWcOQXnJdiwYQMyMzPzHDFJX0bCv94ImLcDtr+MQO0/lyDx8RukhURAzdoE2vXsIMrIxNtZ25B4/9PSePUq5jDulv33ValfW8T//bzI91fSVIdJ3+zTcMJ35z0Biojoa/Y+MBybJq/F+PVTMXXbTAR5ByLsbSj0KxnA3rE6IBDg5Nqj8Dx7R6KdipoqOgzrDKGyEO0Hd4TnmU/lzbo1h1BZiOT4JPT5aaDMMWyZuq7Enxd9XcIDw7B60kpM3/AjZm+fiwDvALx7GwqDSgao7lgDAoEAR9Yewu2ztyTaqaqposvwrhAqC/HdkE64feZTeYvuLSFUFiIpPgkDZ/4gcwxrp6zO/fn149fY+b8dGLloFObtmo+g14F49/YdLKtZwto+OxfWhX3ncWLT8ZL5BZDCiA18j7OTN6Pz+nHosW0KPngHI/ptGLQr6cPC0Q4CAXBn7Sm8OntXop2ymgoaDnOHkrIQToPb4dUZT4nya4v2w8DWFFVb1sU4z3UI8XoNUXomzOpVhY6pAWKDPuDkiNUQZ+XdonV++jb02T8T1o0dMNpjJT68CkJSRByMq1vBwNYUaQkpuDhzR77bBIvTlsqxcpD83NLSEkB2Sh5pclZKybOrSx6qqqqoVq0avL294ePjA0dHxxLptzSUaGDqxIkT8PLywqBBg+Q+tnDOnDmIi4uDqqoq4uLi8OjRIzx+/BgWFhZwd3eXqLt69Wo8fvwYs2fPzt0m2LBhQwwdOhSjR4/G1KlT4ezsjMqVK8PY2BgqKioQCAQSictEIhGysrKQkZGBtLQ0pKWlQUtLC5s2bcozNhUVFZiamuL06dNo3LgxXrx4kSdhWX4rl6ZMmYIpU6bkuR4QECDxe7ly5Qp++uknPHnyBFpaWti6dStGjx6dW75r1y78/fffubm3Xr16hVu3bkFZWRn9+/eX/culUvF+32UkPvWD+egu0G1UE5o1bZARFY/IP24hbOsZJD2TPJ0p1T8MkafvQNvRHh+OFO/URJM+raCsq4W420+R8jq4WH0REVVED67+i3mdZuC7UV1Rx7UeGrRpiKS4RDy8/gCXdp/Dy3+e5WmTkZaOS7vPwaVTM1zdJxn0r1o3ewWzkYUx3Hq0lHl/BqaoJPx79R6mfTcF3Ud1Rz03RzRs0wiJcYnwun4fZ3edwbN/nuZpk56WjrO7zsC1sxsu7D0vUWb3jT0AwNjCBK16tJZ5/88DUwBwYe85vPz3BXqM7YmqtauifnNHpKWk4cFNL5zfew4PbpReXk0q33yvPsSeTj+j8ajvYOtaB3ZtHJEalwS/64/gtfsyAv95madNZloGvHZfhkMnFzzclzcvWVZaBo4MWIr6/Vrhm+/dYOloByUVIeJCIvHk8E38+/vFfLfaAUBqXBL2dV+I+v1aoVaXJjCyt4RxdStE+obCf/8zeG46U+AJgcVpS+VXSW7JK6qcQNOLF9K3Xz9//lyifkHEYjHS09MhEAhyd4YVJCfvlDzJ3MuSQFyCf1MxMTHYuXMnOnbsiFq15Dt2dPz48diyZQuUlJSgra0NExMTNG/eHAsWLEDlypInkvTu3Rt3797F69evcxN/A9l/MefOncO5c+fg5eWFDx8+ICoqCqmpqXK9EDt37owzZ87kuR4WFoa0tLTc4JOvr2+Rk60D2QnbLSwscv/s6emJ1q1bY8CAAVi8eHGevFF79+7NPdUQAJSUlFCjRg0sWrQI33//fZHHURg8lY8qAqenhTuBk6i8GuxUcP5BIkWRJM4o6yEQlYgmAv2yHgJRsc0KPFDWQyhV8SPbF6md7u9XSmwM/v7+sLOzg5WVldSc1ZaWlggPD4efn5/U1D2JiYkwMjKCkpISYmJiJGIjn0tNTYWOjg6UlZWRkJAgkRy9vCnRwFRpy8zMxLNnzwq1BC0zMxOZmZkQiUQFBqlUVFRkRhpLS3R0dIHZ+XMioTmEQuEXfzExMEUVAQNTVFEwMEUVAQNTVFEwMEUVQYUPTA1vV6R2ujtL9pTRTp064fz58zh16hS6d++ep/zYsWPo06cPOnXqhLNn8x7g8l+tW7fGzZs3ceDAgQLzTM2dOxdLlizBd999h3PnzhX7OZSmEk9+XpqUlZULvS9SWVkZ6urq0NTUhJaWVr6PsgpKAdKPjBQIBFBTU8t9lOcIJxEREREREVF5IhaJi/QoabNmzYJQKMSIESNw545krksPDw+MGTMGQqEQs2bNkijr168fqlatihs3JE9jnz9/PlRVVTF69Og8gayMjAz88ssvWLZsGUxMTLB9e/lP2M9IBxERERERERFVPOUg+TkAuLq6YsOGDZg4cSLc3Nzg7OwMGxsbBAQE4MGDB1BWVsbWrVvRrFmz3Da+vr44cuQIAGDnzp1o3fpTjsCWLVti7969GDp0KLp06QI7OzvUrFkTycnJePLkCSIjI1GlShXs27dPIp1QeaVQK6aIiIiIiIiIiOQiKuKjFIwdOxb//PMP+vTpg3fv3uHs2bMIDw9Hv379cPfuXYwYMUKivr29Pfr06QNbW1sMGzYsT399+/aFn58fJk+eDD09Pdy6dQsvX75EvXr18Ntvv+Hly5dwdXUtnSdTwrhiioiIiIiIiIgqnNLYllccjRo1yl0FJQ9ZdS0sLLB27dpijqrsMTBFRERERERERBVPOQtMUf4YmCIiIiIiIiKiiqeUtuVRyWJgioiIiIiIiIgqnPK2lY/yx8AUEREREREREVU8XDGlEHgqHxERERERERERlQmumCIiIiIiIiKiCodb+RQDA1NEREREREREVPFwK59CYGCKiIiIiIiIiCocMQNTCoGBKSIiIiIiIiKqeBiYUggMTBERERERERFRhcMVU4qBgSkiIiIiIiIiqngYmFIIDEwRERERERERUYXDFVOKgYEpIiIiIiIiIqpwGJhSDAxMEREREREREVGFw8CUYmBgioiIiIiIiIgqHrGgrEdAcmBgiqR6KNQs6yEQFdt6p+llPQSiErH3waqyHgJRsbWrP6qsh0BUIpSEZT0CIpKFK6YUAwNTRERERERERFThiEVcMaUIlMp6AERERERERERE9HXiiikiIiIiIiIiqnC4lU8xMDBFRERERERERBWOmMnPFQIDU0RERERERERU4XDFlGJgYIqIiIiIiIiIKhwmP1cMDEwRERERERERUYUjFpf1CEgeDEwRERERERERUYXDFVOKgYEpIiIiIiIiIqpwGJhSDAxMEREREREREVGFw618ioGBKSIiIiIiIiKqcLhiSjFUmMCUWCyGQMAXHREREREREREBYjFjBIpAqaxufPToUTg6OuLo0aPF7is5ORmVK1fG4MGDS2BkZcfT0xPnzp2D+D/rDZ88eZKn7unTp+Ht7f2lhkZERERERESkUMSioj3oyyqzFVORkZF4/PgxIiMji93XoUOHEBISAjU1tRIYWba3b9+iTZs2BZafOXMG33zzDQCgV69eePHiRYF1X716Jdc9ly9fjj///BO3b9+Gq6srACAgIACOjo7o378/Dhw4AADw8PBAt27dMHXqVKxevVrep0RERERERET01RBxxZRCqBBb+TZv3gwA6NOnT4n1mZGRgYCAAGhra0NbWzv3emJiIhITE5Genp57LTAwsNirlz58+IDz58+jevXquUEpANi3bx/EYjHatWuXe83NzQ1mZmY4cOAAli1bBhUVlWLdm4iIiIiIiIioLCh8YOru3bt49OgRAGDs2LEQCoWFam9paYlr164VWD5jxgwsWLAg98+LFi3CwoUL89RTV1dHSkqKxLUmTZrg7t27co1j27ZtyMjIwMiRI3OvZWZmYteuXdDR0cH333+fe10oFGLAgAFYtWoVdu3ahdGjR8t1DyIiIiIiIqKvBXNMKYZSCUwFBgbC1tZWrroTJkzAhAkTpNYJCAiAjY1NvmWbNm3K/dnX11fuMeYoidVGxU28HhcXhzVr1sDQ0BCjRo3Kvb57924EBgZiwoQJ0NTUlGgzadIkbNq0CQsWLMCAAQMkVnVR6TKwt0C9kR1h7VYHGsa6SItLxoenb/Fs9xWE3H5epD4FSgLUGtAaNXq6Qr+aOYQqyogPicDbi154sv0C0hNSpLav1qkxWq8cifTEVOx1lv7v6XOWzWqj84GfoKQsxKEWPyL2bViRxk8Vg6W9FTqO6II6rvWgZ6yPpPhEvH3yBpf3XMDzO3lz3cmr8XdN0axrc1hVt4Z+JQO8DwxH0KsAnNlyCqG+IYXqy61nK4xdPQkA8IP998hMzyzyuIj+Kyz8AybPWYyXPm+wbfUvaNbYqayHRF8xG/vK6D2qF5zcnGBobICEuAR4P/HBqd1/4sHth0XqU1lFGd/164iWnZrDwsYCeoa6iAiLxLvAdzh74BzuXP4n33brTqxGPZe6Mvv3OPcXFo5ZXKSxkeIysrdAwxEdYeNaB1rGukiNT0b4k7d4sOcKAu8UfW5cr39r1O7pCsOq2XPjuJAI+F7ywr+/Fzw37ndsHqwbO8js3/v8PZwZtyHP9Uq1beA83B0mDtbQtTSGklCI+NBIRPm9w6O9VxF8jzl+FQ1P5VMMpRKYUlFRgYOD9DeEmJgYvH//HqampjAwMJDZX35u3LiBQ4cOwcTEBN7e3jA0NJR7jA8ePICzs7PEFrmiiouLg7GxcZHbr1mzBjExMfjtt9+gq6sLAEhPT8cvv/wCNTU1zJo1K0+bypUrY+LEiVixYgV+/vlnrFmzpsj3J/lV+dYJbTeMg4qGGqJ8gvHh6VtomujDpnV9VGnXAPfX/oH7q04Wqk+hmgq+2zsDVs1qIz0xBeFevsjKyESlulXQcEp31OjRDGf6/Yb4oIg8bZU11OA8uRsajO8MAEhPTJX7vjrWJmi/eQKUlAu3ypAqJuf2jTB+/TSoaagh2CcI/s8eQM9EH46tneDUrhFOrTuGE6sPF6pPdS11jFk9GY3cXZCalILAVwEI9gmCqY0Z3Hq2QtOuzXF81SGc2XxKrv6q1rXD8CVcIUql485dL8xZvBLRsXFlPRQiuH7bFPM2zoG6hjr8vf3h+fQ1DE0M4NKmMZq1b4q9a/dj98q9herTxNwEa4+vhKWtJeJi4uH30g/ej71hbGYMJ9cGaNyqER7ceYiZg+YgM0My6G9sZoyszCxcP31D6j28n/gU+rmSYrNr74TO67PnxhE+wQh/9hZaJvqo2ro+7No1wD/r/sCd1YWfG/faMwM2TbPnxiFevhBlZMKsbhU0ndwdtXo0w9F+vyEuOO/cWNvMAKLMLLw64yn1HmFP3ua51nr+QDgPdwcARHgHI9TrNcRZIuhYGKFGh4Zw+K4xvM/dw9mJGyEWifO0p/JJzL8qhVAqgSkLCwuZCb83bdqECRMm4Oeff8b48eMLfY+IiAgMHDgQIpEIK1asyBOUysrKQmJiIlRVVaGhoZGn/fHjxwEAHTt2LPS9P5eYmIigoCC4uLgUqb2fnx+WL18OZWVlTJw4Mff6ggULEBQUhEmTJsHS0jLftnPmzMGBAwewdu1aNG/eHN27dy/SGEg+uramaLtuLAQCAS6NWoe3F+/nlhk6WOG7PTPQcEp3xPiG4s0Z+bZwAoDrwkGwalYbQX89xdXxG5EWlwwAUFIRotGMXmgwrjM67JyGY+5zIc7KPiJCw1gXThO6okYvV6jpaSH49nNYu9WR+57KGmrosGMKhKrKSE9IgapO3n8j9PUwtTHDuLVTIBAAa8Ysw/2Ln16/1jUq48fd89Bjcm+E+gbD8+wdufvtN/sHNHJ3wbPbj7Fp8lrER336wG/vVAPTts1C7x8HIOhVAB7flP7tv56JPqZun4nE2EQYmpXcQRdEx09fwMHjZ/DGPxBWFmbQ1NRAyLvwsh4WfcUsbS0wd/1sCAQCzB+1CLcu3M4tq+JQBUv3/orBUwYh8HUgbpzxkKtPNXU1LN33KyxtLXH895PYuXw3UlM+fZllZm2GRdvmw8m1AUbPGYlNi7ZItDc2M0JEWASWTF5WIs+RKgZ9G1N0WjsWEAjw55h1eP3Z3Ni4hhV67Z6BppO7I9I3FN5n5Z8bt1kwCDZNa8P/r6c4M2Ej0uI/zY3dpvdC47Gd0WPHNOzp+GlunEPb1AAJYdE4P3VroZ5L7Z5ucB7ujkjfUJyduAkRr4LyPNeOq0bBoVNjfHgViLsbzxSqfyo7XDGlGJTKegBFIRaLMXjwYISFhaF58+YYPHhwnjp37tyBvr4+pk2blm8fJ06cgLa2Ntzc3KTe68WLF/D29oa4gFDroUOHkJ6ejk6dOuUpy8l3VVCQTiwWY+TIkUhJSYGysjK0tLQAZOfNWrFiBczMzCTyW/2Xvr4+jhw5AqFQiKFDh+LJk6JvtSHZ6g3/Fipa6ri34rhEUAoAor1DcGXcRohFIjhN7Cp3n5qV9OHQpzmS3sfg8uj1uUEpABBlZOHub0cR5PEURg7WqNL+05YS0wZ2qDv8WwCAx8yd8PhpR6GeS+tVo2DkYI3rU7chNSahUG2p4ukwvDPUtTRwbOUhiaAUAAT7BGHDhFUQiUToOqGX3H1Wq2ePNgO+RVxELNaOWS4RlAIA3wc+2LtwB5SUlNB+sPQvCIQqypiy9SfoGOhizWh+KKKStWbLbvgFBMG9TXMc27UB5qaVynpI9JXrNaIHNLQ0sHP5bomgFAD4e/tj0djFEIlEGDhpgNx9Nmnrgmo1q8Lr1gNsWrRFIigFAOHB4Vj50+qPdRtLlOnq60JNXQ3vQz8U8RlRReU8/FuoaqnjzsrjEkEpAIj0CcGZCdlz4yYT5J8ba1XSxze9myPxfQxOj12fG5QCsufGfy09irceT2HiYA379pLbrdX1taGiror4d1GFfi7VWtUDAFyduztPUAoAYgPf5wa7qrVxLHT/VHZEYkGRHvRlKWRgau3atbh48SKqVKmCY8eOFbr9o0eP4OfnhzZt2kBVVVVq3ePHj6NmzZowMjJCp06dcPXq1dyy169fY/bs2TA2Nsbw4cPztK1RowYAoFatWhAIBOjXr1+e53Hz5k2Ja8HBwfj++++RlZWFrVu3ytye2Lx5c/z222+Ii4tDq1atcP/+fan1qWiUNdVQo6cbMlPT8WLf9XzrvH/4Bu8fvoGRgzXMG9WQq99a/VtBqKKMl4duIiMp/214T36/AACo80Pb3GtJ4TG4OeN37HWeiJeHbhYYOM1PgwldYNe5Me6vPgX/S15yt6OKSU1THa49WiI9NR3X9l/Kt47vQx+8efQalR1sUKNhTbn6rdX0GygpKcHj6DWkJOafB8L735cAgCrf2Enta8iiEajhXBM752yF3+PC5xIkkmZg7244f2QHVv5vNnR1mK+RypaGpjra92yH9NR0nN53Nt86Lx++wsuHr1DVoQq+aSTfamlLWwt4P/bB2YPnCqzzLjA7z2QlC8ngrIl5drqK96Hv5boXfR1UNNVQu0f23PjR/vznxu8evsG7R29g4mANq4byzY3r9cueGz85fBPpBcyNvXZkz40dB7WVuK5jnv25KT40Ut6nkUuUlSWzTu58m3vDFIpYLCjSg76sUjuVLykpSa6E3LKSn3t4eKBFixYS15o3bw4HBwecPn0apqamhR6bo6Mjnj59CpFIJLNunz59UK9ePdy/fx/3799HSMinRL3r169HfHw8jh8/nm8AadGiRYiJicldcWVhYZFbdv36dfz444+ws7NDXFwcEhISEB8fj/bt2yMkJAQTJkxA167yfbvw448/Ijk5GQsXLsQvv/yC06dPy9WO5GfVrDZUdTQQ5PEUmanpBdYLvPEYZs7VUaV9A4T9KzvPQs4qqKCbBa92C73rjYykVFi51oaKljoyklIR8dQfEU/9C/08bFrXR6MZvfD24n14rfuz0O2p4qnd9Bto6mjiicdDpEt5bT+68QDVnRzg1L4RfO5L36oNAFbVrQEA796+K7COjoEOACAlMbnAOm0Hfos2A77FhR1ncPvkzQLrERXVuGHyrzohKm2OzRyhpaOFfz3uIy01rcB6d6/fQx3n2nBt3xTP/pWdXPrgxsM4uFF6nkDn5g0AAG9e+ElcN84NTH2Atq4WBk0eiDrOtWFb3QYxkTEI8AnEH3tPFzkhOykmm6a1oaajgbcy5sZ+Nx7D0qk67No3QMh92XPjnFVQbz0KnhsH3/NGelIqbJrVhqqWem4AS8csO29x/LsoqOlqoumkbrBwsoexvRWSI+MQ+ToED/ddyzch++MDN+DQyQWtFwzE2QmbEP2fA4F0zA3x7W/ZCxGeHPaQ+Tyo/GAcUTGUWmBKWVkZQ4YMKbD81atXuHfvHho3boyaNQv+Bt7MzCzPNScnJ7x48QJKSp8WfCUmJsoVCMvKykJGRga++eYbmXUBoGbNmpg9e3bun+fOnYslS5YAANatW4cBAwagSZMm+ba1srLCqVN5k/omJCSgd+/e0NHRwblz5+Du7o6EhATo6uqie/fuePXqFTp27Cj19/I5Nzc3bN++HRYWFujZs6dcbahwtD5+AxPtEyy1XpRPiER92f0aSLTLjyg9E7H+4TCpYwstMwPE+hXt5Dy9KmZou34sYnxDcW1K4fbdU8VlaG4EAAh+nXfZ+udCfLLLjcyM5Oo3J6l5ZGjexKQ5nL/N3i7i9/h1vuU1nGvihwXD8ez2Yxz8tXBJfomIFJGJuQmA7C170vj7BEjULyplFWUYmxmjaTsXDJs+BEkJSfnkl8oOTFnaWmL39R1Q01CD92MfPLn7FDb2NnB1bwZX92Y4s/8sVs9eV6zxkOLQ/jjXjXwtfW4c+XGOq2Mm39xY+2NwKVLK3DgrPRMxAeEwrW0LbTMDRH+cG2ubZrc1sDHFsCtLoayhirDHbxF87xWM7C1h/60z7L91xuMD13Fl7m6JPkPu++Dk8FVoOacfhl9fhg+vgpDwLhqiLBF0zA1hWtsGSZFxuPjT73h+/JZcz4XKB27LUwylFphSU1PD7t27CyzftGkT7t27h0GDBhUp+fnnQamLFy9iwIAB2LVrF7p161Zgm6dPn6JNmzbo3bs3Nm3aVOh7ApDY+nf27FmJoJU0R44cQb162XuXdXR0oKenh23btuVu98uxZMkSZGVl4c8//4S3t3zHkVarVg0AMHLkSLnqU+Hl/CeZGpsktV5qdMLH+rL/81VSEULDUAdZ6ZnITC74W1GJfs0NixSYUtHWQMedUyEWiXBx+GqZ96Ovh+HHQFNSbKLUegkx8dn1zeU7gTQypOCAFADY1K6CbhO+R0ZaBs5t+zPvuMyNMGXrT4gKi8L68asglmOFKxGRojP++J4cHys9/2NcdHbevqIGprR0tHD+leQK+1sXb2Pbkh0I9Q+VuJ6zla/Fd27Ys3ofjm49hvS0jNxytw6umLF8GroM6oznXi9x5eRVUMWnI+fcOOVjLlMdOb60VVIRQvPj3DhDxlw15ePcWMfMMDcwlXOP6h0b4e+1p/DvtvPI+uy1au/uDPelw1F/YBuEPvDFi1OSB7rEh0Yi+m0YTGpYw7S2LUxr20qUR/uFIfJ1wQEzKp+4LU8xlFpg6ksRi8WYN28e4uLi8gR5/qtu3bqoXr06tm3bhjFjxsi9aqogcXFxcgePUlMl90hfuXIFdnb551URCoXo2bNn7j7m06dPo1u3bvjtt98wa9as3Hrx8fHQ09ODQMB/bKVN/eOWo/SE/HPl5MgpVzfUkatPgZIS0hOl/4de2H7z03b9WOjamuLcoBWID5IeMKCvizzb6QAg+WPy0Zz6xVG/tRPGr50CFTUVHFqyF4EvAyTKVdRUMG37LKhqqOHX/guQFCc9aEZEVFHoGeoBAJJlvCcnJSR9rK9bpPtkZmTiysmrUFZRgYm5Mezr2MGlVWNEhkdh8/+2IjMjM7duWkoaXjx4iVsXbuPotuN5+rp98Q5UVJQxf/M8jJs/Gtf/vI6sLH6ZUNFpfJwPpBWQRzJHWnyKRH1ZfQqUlJAmx9w47ePcWOOzuXFGSjpCH/ri9cX7uL/9Qp42vpe8IFRRRpeNE9BqXn+8PP1P7ql+pnVs0efQbKjraeHVGU883HsF0W/DkZWRCUNbM9Tq4QrHQW3Q78hc/DF6Lfw9nsocI5UP3MqnGEo8MOXv74+OHaWfsAQAMTExAIDFixdj48aNUut+8803BSY5/+OPP/Dw4UMMHDhQrq1va9asgYuLCyZNmpQn8fjnUlKy3+wiIyNx5coVvHjxAi9evMCNGzdy6wwePBh9+/aFjY0N7OzscOdO3mPUW7ZsiSdPnuQJghUUlMpPTkLzhg0bSlzPzMyeNBQ1MCVPu01WFSv3RoMJXWBgZyG74kepMQn4e9HB3JPrVHU0pNbPKc9Z4SSrb7FIBFVt6X0Wtt//ajSjF6q0a4Db8/ch9O8XhW5PiqHr+J6wsLOSu35CdDwOLN6NhI+vbQ1tTan1NXWzyxOKcYqjmoYa+s0ZjPY/dEBmRiZ2/7wdV/ddzFNvxNJxsK1TFWtHL0OIjC2GRETl0YAJ/WBjX1nu+nHR8di0aEvuSihNGe/JWjpaue2KIi01DUsmfzrl1MBYH9OXTkWPod2gZ6CLxROW5JYd3nwUhzcfldrfjTMeGDV7BMyszWBpa4kgP+nbu6j8cBnfBUaFmBunRCfgxuKDuSuh1GTMY9V0s8tT5Jg/pHycG8vqEwDUPs6NUz6bG/+79Rz+3Vpwgn8A8D57Fy1m9oGetQkMbE1zV1t9+9swqOtpwWPJYfy77bxEm/Bn/gh/5o/AO8/Rc9d0uC8bgR0tf0RGCncgKAJu5VMMJR6YSk9Pl3sVEQC8f/8e799LP+XD2Dj/rSMikQjz58+HUCjE/Pnz5bpfo0aN0L9/fxw8eBAnTpxAr175H38eFpb9JrVx48bcwJmBgQE0NCTfKNXU1DBu3DgsWLAAt2/fhpubW26Zh4cH/vrrL0ydOhWamtInGNL8+++/EAgEcHZ2lriekZG9NFXWyYL0ibVbHVg2rSV3/figD/h70UEkhmcHUtUNpOcxy1nRlBgeLbNvUUYWUqIToGmsl5vUXGa/YbL7/VzVDg3hNLELXh39C892XylUW1IsddzqoXYT+VeBfgh+jwOLdyM6PPtIZW0Z32TqGGR/Kx8dVviTbgCgWn17jFszBeZVLRDsE4St09bB//nbPPU6jugCtx4tcXzVIXhd+bdI9yIiKmvOzZ3g2LS+3PXDgsKwadEWRH58T9Y1kL4SKmdlVURYyayCjomMxS8Tf8Ohv/ehTbfWOLDxsMw8V/8V+CYIZtZmqGxfmYEpBWLrVgeVm8g/N44N/oAbiw8i4ePcWEPG3DhnpVSCHHNYUUYWkqMToGWsJ5HUPN9+P86NE+SYc/9XlN876FmbwMjOEtF+YahUqzLM6lZFfGhknqDU5/yuP0LgPy9g07Q2qraqB58LnKcoAm7lUwwlHpiqUaOGXEfXb9q0CRMmTMDGjRsLzDEVERGBSpUqFRh42bt3L168eIGhQ4fC3t5e7jEuXboUp06dwo8//oiuXbtCRUUlT51377JPkurVqxcGDRqE+vXro3Llyli0aBEWLlwoUXfs2LH47bffMHfuXNy8eRNCoRCZmZmYPXs2BAIBxowZI/fY/is5ORmenp6oWbMm9PT0JMrS0rKj9EUNTMnz97TZemCR+i6vTvdZIrtSPpI+/mdqWF36ihSjGlYS9WX3GwNNYz0YVrfE+0d++dZRUlWGfpXsQwCSPk4C5OU0sSsESkpQ0VJHm7X5vw41jLInv03m9UP6x+1a15kcXeH82le+4Px/RYdlfwiysreWWs+qRvY3/1EfPzQVRvdJvdFjcm8IBMCZLadwYvVhZKZn5lu328ReyEjLgEU1S4xdM1lqv6NXToQoS5S7+ouIqLyY2ntGkdrlBJqq1LCVWi+nXJ7AlFCohJGzRwAAdi7fjYz0jHzrpaak4tVjbzRr3xRVHarkBqZUVLPnyQW1y5GVmZX9A/fNKJQjfYs2N875stTIXvrc2Pjj3FjeAFJieAy0jPVgVN0SYQXMjYWqyjCwNcut//l1IDs5ujSij6/VnM9C+rbZp7xHSEm4nuPDyyDYNK0Ng6p5D+ii8okrphRDuc4xlbPdz9Awb7K8wMBATJ06FVpaWnKvlsphZWWFESNGYMOGDTh8+DB++OGHPHVyVkyNHj0abdu2ldqfiYkJli9fjkmTJmHSpEnYtGkTpk2bhrt372LcuHGoXr16ocb3uVOnTiExMRG9e/fOU5aTt4orpkpf6D8vkZ6QAgsXByirqxZ4LG7lVvUBAP5X5Dsy2f/KA5h8Y4vKreoVGJiyaOwAFS11hNx5IXVVVb4+vg/bdWoss2qVdg1yf2Zg6uvx0vMZkhOSUdOlNlTVVZFewGu7fqvs18eDQqxiEioLMXrlRLh2b4GIkA/YOHE1fB9KPypaIBBARU0Fzbq1kNl/s67NAXxa/UVEpOge/fMYSQlJqOdSF2rqakhLzX+rUOPWjQAAd678I7PPrCwRGrdsiCoOVXDzjAd8nuZ/EioAaOtmr375POB16J/9MDEzRl+XAQgPKXiXg13t7MN4vJ9If5+niiHI8yXSElJgLWNuXPXj3PiNnHNj3ysPYFrHFlVb1iswMGXVyAGqWuoI/PuFxKqqUbdXQ8fMEFubTUF8SMErvCvVsgEAhD/JXrmds5pL00h2zjYtE72PbQr3ZTERSacku0rZiY7OfpP471Y+kUiEQYMGIS4uDitXroStrW2h+546dSqEQiGWL1+e78qhnBVTtWvXlqu/iRMnYujQodi8eTO6du2KDRs2oH79+li9enWhx/a5HTt2AAAGDMib6ykxMTshsK5u0RJfkvwyklLhc/I2lNVVUWtg63zrmDpWg5mTHaK8gxH2r3yTspeHbiIrIxM1+7WEsqZavnXqjXAHADzfd63Q4z7eYR42Ww+U+ogP+gAAONTix9xr9PVITUrFnVMeUFVXQ5sB3+Zbx86xOuwb1ECQdyB87r+Su++eU/vCtXsLBL0KwPxuM2UGpQBgZN1B6G/TXeojxw/236O/TXdMcS36qlQiovIkJSkFV05ehZq6GjoP7JRvnVoNaqK2Uy289fbHs3+fy9XvI88nAICOfTsUWKdyNWvUrO+AzIxM+D5/k3v9vocXAKBt9zYFtv2uf0dUsqgEf2//3O2IVLGlJ6XixanbUFFXRf0B+c+NzR2rwbKBHSK8gxFyX7658ZPD2XPjun1bQqWAubHzx7nxo/2Sc2P/W88AALW6NS2w/7p9W0LXwggRPsFIfJ8dXIp4FYysjExUqlkZJg4FryDXMtFDlebZaRPePyvcVlcqO+IiPujLKteBKR+f7Dew/56299tvv+H27dtwd3cv8ja5KlWqoFevXnjx4gXOncubJO/evXswMDCAubm53H1u2rQJVlZWOHPmDJSUlLB//36oqeX/hiqPPXv24K+//oK7u3u+ydJzAncMTH0ZT3ZeRkZSKlxm9kZVd8l8X4YOVmi/eSIESkp4sOF0nrZN5/XDD/fWwa5rE4nryR9i4X30FrTNDOG+bTLU9D7lIlNSFsJldh/YtK6PKO9g+F95UDpPjL56F3eeRWpSCvr8NAAN3V0kyqxrVMbEjdOhpKSE0xtP5Gnbf+5gbPD8HU26uEpcr1bfHp3HdEdKYgqWD/0FcRGxpfkUiIgqjBM7TiElKQUjZw2DWwfJ99YqDlUwf/M8KCkp4cD6g3najp03Csf+PYTWXVtJXD+06TCi3keh6w+dMWjyAKiqSaaxqONcG7/sXARVdVXsWL4LKUmfTlo7uu0YEmITMHT6YHTs657n8JzOA77DhAVjkZaahsUTi7YtjBST187LSE9KhdtPvWH/n7mxcQ0rdNmYPTf23Jh3btxybj+M8VyHml0k58ZJH2Lx7Ngt6JgZouuWyVDTlZwbt5jVB9Va1UeEdzB8/zM3vr/9PFLjkuA6tSe+6dMC+M9rtV7/1mg9fyAyUtNxbtLm3OuZqem4s+okhKrK6LZtCio3yXugVqXaNuixczo0DHTw/MRtRL6Wve2PygeRWFCkB31Z5WYrn1gsRkhICAwNDaGhoYG7d+/if//7HwCgadNPUe/Y2FisWLEChoaG2LVrV7HuOWPGDBw9ehTLli1D586dc6+/fv0aDx8+RPPmzeXu6+XLlxg1ahRCQkKgoqKCjIwMfP/999i+fbtEQnR5+fn5YdKkSdDT08P27dvzrfPsWfa3ApaWloXunwovPuA9rk7ajHYbxsH99ymI8g5GrF8YNCvpw7SBHQQC4P6aU3hz5q5EO6G6CuoOd4eSshDfDG6HN6c9JcrvLNwPPVtTVG5ZF4PurkPY/dcQZWSiUr2q0DI1QFzgB1wcvjr3OFuikvY+MBybJq/F+PVTMXXbTAR5ByLsbSj0KxnA3rE6IBDg5Nqj8DwrefKoipoqOgzrDKGyEO0Hd4TnmU/lzbo1h1BZiOT4JPT5SfYqvC1T15X48yIiUkShAe/wy8TfMG/jbCz+fSHeevsj2C8YhpUMUatBTQgEAuxZsw83znhItFNVV0WvET0hVBai+5CuuHH60+nTkeFR+HnkQizf/xuG/zgUPYd1x5sXfkhNToVlFUtUqWGLrKws/LHnNI5skTwJO9A3CD+PXIhfdizCTytnYPDUH/DW2x9KSgJUrVkVJmbGiI6IwepZa/D2FVeRfE1iA9/j3OTN6LR+HLpvm4II72BEvw2DViV9WDhmz43/XnsK3mcl58bKaipwHpY9N3Yc3A6vzkjOja8v2g8DW1NUbVkXYz3XIcTrNbLSM2Feryq0TQ0QG/QBp0bknRtH+b7DH6PWovv2KeiwfCSaTe6OCJ8QCJQEMHGwho6ZIZIi4nB5zi5EeEsm6L+35Sz0K5ugbt+W6HtkLmL8wxET+B6ijCzo25rCqJo5BEpK8Lv5GFfn7SmV3yeVDiY/VwzlJjAlEAjQpEkThIaGQklJCSJR9htN9+7d0ahRo9x6+vr6uH//PgIDA6WuZnr1Knu7iZJSwYvCnJ2d0aZNGxgYGCAlJSX3xL3Dhw8DAPr06ZOnTWxsrMSffXx8sHHjRmzfvh3p6eno1q0btmzZguPHj2PmzJlo0aIF+vfvj5kzZ+Kbb+Q7McvHxwft2rVDQkICDhw4AGvrvEtK4+PjsW/fPgBArVryn6RBxRNw5SGOd/wZ9Ud/B2u3OrBt64i0uCQEXn+EpzsvI/Sfl3naZKVm4Omuy7Dr7ILn+67mLU/LwJn+S1Grfys4fO8GU0c7CFWFSAiOxMtDN/F4+0VkJKbkaUdUkh5c/RfzOs3Ad6O6oo5rPTRo0xBJcYl4eP0BLu0+h5f/PMvTJiMtHZd2n4NLp2a4uu+iRFnVutmrPI0sjOHWo6XM+zMwRUT0yd9X/sHoDuPQZ8z3cHJzQpO2LkiMS4TntXs4ufMUHv3zOE+b9NR0nNh5Cq27tMSfe8/kKX/58BX6NhmAnsN7wLm5E6o6VIG6pjpC/ENx5eRVHN16HH6v8p6WCgCPPZ+gb9OB+H5kTzRs7oxajg4AAP/XAbh8/AoObz6KpISkEv0dkGJ4c/Uh9nX6GY1GfQcb1zqo1sYRqXFJ8Lv+CA92X0ZQPnPjzLQMPNh9GQ6dXPCogLnx0QFLUa9fK9T53g0WjnZQUhEiPiQSTw7fxP3fLyK9gLlx8N1X2OY6FQ1HdICt2zcwr5+d+yzqdQien7iDe1vPIj0h/7aXZ+/Co/3X4DyiAwyrmsOsblUIlYWIDf4Anwv38fjg9XyfD5Vv/GpfMQjE8hzNVgryO5Xvxx9/xMWLFyESiWBnZwdXV1dMnz4dQqFQZn9BQUFo3LgxtLS0oKqqCm9vb4jFYqxevRpTp04tsF1mZiaUlSXjczVr1kRQUBDCwsKgq6uLp0+fYt++fRAIBNi9ezeioqLg4+ODSZMm4cqVKxCLxbC0tMTSpUsxcOCnlQGvXr3CmDFjcOvWLQBAly5dcPLkyTz3q1KlCsLDw5GSkoLQ0FA4OjoiMjIS69atw8SJE3PrjR49Gjdv3oSmpibevn2LhIQEmJubw8/PLzeoVtKYb4gqgjtKnCxTxbD3waqyHgJRsbWrP6qsh0BUIjoKeTIbKb6fAg+U9RBK1S2z74vUrnn48RIeCUlTblZMAcCKFSuwYsWKIrWtXLkyUlNTER4eDgBQV1dHmzZtMGLECKnt/hskAoDJkyfj0aNHubmbjI2NsWpV9ocBXV1dzJs3D9WrV0flypXRsGFDTJkyBb169YKKiuR+/Zo1a+Kvv/7C1atXMX/+fNjZ2eV7v89ZWlqiW7duaNasGQYPHpynv507dyIrKwvKyspo0KABNm/eXGpBKSIiIiIiIiJFJWImc4VQZiumyruMjIw8gab/ysrKkms1V478VmcVRWZmJgQCQaHuXVRcMUUVAVdMUUXBFVNUEXDFFFUUXDFFFUFFXzF1w7R3kdq1fn9MdiUqMeVqxVR5IisoBaDQgaGSCEqVZD9EREREREREFZUYTH6uCBjhICIiIiIiIqIKh8nPFQMDU0RERERERERU4XDFlGJgYIqIiIiIiIiIKhyumFIMDEwRERERERERUYXDwJRiUCrrARARERERERER0deJK6aIiIiIiIiIqMJhjinFwMAUEREREREREVU4IsalFAIDU0RERERERERU4Yi4YkohMDBFRERERERERBWOuKwHQHJhYIqIiIiIiIiIKhyeyqcYGJgiIiIiIiIiogpHJOBWPkXAwBQRERERERERVTjcyqcYGJgiIiIiIiIiogqHW/kUAwNTRERERERERFThiLiTTyEolfUAiIiIiIiIiIhKmgiCIj1Ky927d9GnTx9YWVlBXV0dVlZW6N+/P+7fv1+k/lJSUrB06VI4OjpCV1cXenp6cHJywooVK5CWllbCoy89DEwRERERERERUYUjLuKjNGzevBmurq44duwYzM3N0alTJ5iamuLw4cNo2rQptm/fXqj+IiMj0bhxY8yePRvBwcFo3rw5XF1dERAQgJ9++gkuLi6Ijo4upWdTshiYIiIiIiIiIqIKRyQo2qOk3blzB5MmTYKenh5u3bqF+/fv48SJE3jw4AFu3rwJbW1tjBs3Dn///bfcfQ4YMADPnj3D4MGDERQUhHPnzuH8+fMICgrCgAED8PjxYwwYMKDkn0wpEIjFYiaqpwLVNm1c1kMgKjY7NZOyHgJRiYgTpZb1EIiK7erjwn0jTFReaVi4lfUQiIotMz20rIdQqvZZDixSux9CD5ToODp16oTz58/jxIkT6NmzZ57yI0eOoF+/fujUqRPOnj0rsz8vLy80bNgQ9erVw4MHDyAUCiXKMzMz4ejoiOfPn8PLywtOTk4l9lxKA1dMEREREREREVGFIyrioyT5+/vj4sWLsLa2zjcoBQB9+vSBhYUFLly4gICAAJl9btq0CQAwefLkPEEpAFBWVsbEiRMBZG8hLO8YmCIiIiIiIiKiCqc85Jg6e/YsRCIROnbsWGAdgUCAjh07QiQS4dy5czL7PH36NADA3d29wDo598upW54pl/UAiIiIiIiIiIhKWmnkiyqskJAQAECdOnWk1sspz6lfkOTkZMTExMDIyAjm5uYF1rOysoK+vj6ioqKQkpICDQ2NQo78y+GKKSIiIiIiIiKqcIq6lU8gEMh8yCs0NDuPl6GhodR6xsbGAGQHpuTt7/M+c9qUVwxMEREREREREVGFUx5yTEVGRgIAdHV1pdbT09OTqF/c/grTZ1njVj4iIiIiIiIiqnDERdzKJxaXXKapnFVL8fHxUuvFxcVJ1C9uf4Xps6wxMEVEREREREREFU5Jr34qCktLSwBAVFSU1Ho5q5qsrKxKpL/P+8xpU15xKx8RERERERERVTjlYStfTqDpxYsXUus9f/5con5BNDU1YWBggOjoaISFhRVYLyQkBLGxsTAyMirXic8BBqaIiIiIiIiIqAISF/FRkjp37gwlJSVcvHhRar0LFy5ASUkJnTp1ktln165dAQCXLl0qsE7O/XLqlmcMTBERERERERFRhSMSFO1RkqpUqYIOHTogKCgIf/zxR751jh07hnfv3qFjx46wtbWV2ef48eMBABs2bIBIlHeNV2ZmJjZs2AAAGDt2bNEH/4UwMEVEREREREREFU552MoHALNmzYJQKMSIESNw584diTIPDw+MGTMGQqEQs2bNkijr168fqlatihs3bkhcd3Z2Rvv27fHo0SMMHz4cycnJuWVJSUkYOnQonj17Bnd3dzg7O5fCMypZTH5ORERERERERBVOeUh+DgCurq7YsGEDJk6cCDc3Nzg7O8PGxgYBAQF48OABlJWVsXXrVjRr1iy3ja+vL44cOQIA2LlzJ1q3bi3R58GDB9GmTRvs2bMHZ8+ehYuLCwDA09MT0dHRaNCgAQ4ePPjlnmQxcMUUEREREREREVEpGjt2LP755x/06dMH7969w9mzZxEeHo5+/frh7t27GDFihER9e3t79OnTB7a2thg2bFie/oyNjXHv3j0sW7YM1tbWuHXrFm7dugUbGxusXLkSnp6eMDQ0/FJPr1gEYrG4pHN7SRUaGorY2FhUrVq1VDLDR0REIDU1FdbW1iXSX1paGv755x80adIE6urqudefPn2Kx48fo1evXtDU1CyRe5VHtU0bl/UQiIrNTs2krIdAVCLiRKllPQSiYrv6eHtZD4GoRGhYuJX1EIiKLTM9tKyHUKpWVh5YpHYzgg6U8EhImi++lW/evHnYs2cP7t69i8aNCx/0ePDgATw8PDBt2jQIBHmzki1btgyrVq3CrVu34OZW/P8s1q1bh5kzZ2LhwoVYsGBB7vUTJ05g8eLFyMzMzDd6KcucOXMKTHxWFN27d8eSJUtKrD8iIiIiIiIiRVbSicypdChUjqmsrCwMHz4cT548gYeHB/bu3SuxNE0sFuP48ePQ0dHJ3V9ZHBEREfj111+hpqaGMWPGSJRNmzYN69evx5YtW4oUmAoLC4O3t3exx/h5f0RERERERESUrbzkmCLpFCrHlFAoxM2bN9GlSxecO3cOLi4uCAoKyi2/e/cugoKCkJCQAFVVVQgEApmPkydPFni/BQsWID4+HmlpaTAzM5NoZ2BggLi4OHh5ecm8R1RUVIH3CAgIgFgsLvIjICCgJH/FRERERERERBWCuIgP+rIUasUUABgYGOD06dNYvHgx5s+fD1dXV/z111+oUqUKjh49CgCwtbWVyAcljY6OTr7XPTw8sH37dhgYGGDy5Mn51jlx4gSeP3+OKVOmQF9fv8B7lEQOqsuXL8PLywtjxoyBkZFRsfujklWtehX8MKYfmjZvBCMTQ8THJeD545c4uOM4PG/9W2L3aezmjO1H1kFZWRnfNf0eAX5BshuVQFv6OljbW6PriG6o5+YIfWN9JMYn4s0TX5zbcxZPbj8ucr/NOrmiedcWqFzdBgaVDBAeGI6AV/44ufk4gn2DC2xnYmmCXhN6o2rtarCqZoWsrCyE+b/DPxf/wfndZ5Gell7kMVHFZmNfGb1H9YKTmxMMjQ2QEJcA7yc+OLX7Tzy4/bBIfSqrKOO7fh3RslNzWNhYQM9QFxFhkXgX+A5nD5zDncv/5Ntu3YnVqOdSV2b/Huf+wsIxi4s0NqL/Cgv/gMlzFuOlzxtsW/0LmjV2Kush0VesZk17TJk8Cm3bNIepqTFiYuLg9eAJNm3ahWvXbxe53+ZuLpg4cTgaN2oAAwM9hIV9gJ9fAHbuPow//riArKysAtv26tUZ/fp2Q62aNWBuXgl+bwPx9OlLLF+xEa9e+RZ5TFT+iBhmUggKF5jK8fPPP8PY2BgXL16Eubk5YmNjsX//flhYWOD169dQUVEpct+PHj1C165dkZWVBVNTU4ncUp/z9vbODUzZ2NgU+X7yOHfuHDZu3IhevXoxMFXOtO7QHMs3L4aGpjp8vf3w4ok3jCsZonnbZmj1bXNsWbUTG5cXP9GrZWVzrNr2K5SVC//Ptjht6evQuL0Lpm+YATUNdQT6BOLNU18YVDKAU2tnNGrXGEfXHsah1YU7blZDSwOTV09Fkw5NkZKUgoBX/gjyCYSZjRla9WyN5l1b4OCqAzi56Xietu4DO2DovOFQ11THO/93eH73GTR1NGFbswqGzBmKb/t/i5/7zUVEaERJ/QqognD9tinmbZwDdQ11+Hv7w/PpaxiaGMClTWM0a98Ue9fux+6VewvVp4m5CdYeXwlLW0vExcTD76UfvB97w9jMGE6uDdC4VSM8uPMQMwfNQWZGpkRbYzNjZGVm4frpG1Lv4f3Ep9DPlSg/d+56Yc7ilYiOjSvroRChS5dvcWDfJmhqauD5C288ePgEZqaV0LFDG3Tu1B6//LoGCxetLHS/8+ZOwfyfpwMAfF774e69BzAzrYRWrZqhXbsWuHjxOrr3HIbMTMn3ZG1tLezetRbdu3VEYmISnj59iRcvvVG1qg0GDeyFfn27YcHCFVi2fGOJPH8qe9zKpxgU+lPq2LFjMXbsWADZ2+6io6OxbNmyYgWlfH194e7ujoSEBKirq8Pb2zvfJOufs7W1LbDM29sbNWrUKPJ4qHyrbGuFZZsWQSAApgybhavnb+aW2deshi0HVmPs9OHwe+2Pi39eLfJ9NDTVsWHPCqiqqSAxIQnaOlpfpC19HcxszDF13XRAIMDS0UvgefHTyg+bGjb4ec8C9JnSD8G+wbh99pbc/f4wewiadGiKx7cfYfWklYiL+vQhycHJAbO3z8XAHwch4KU/Htz0yi2r26weRi0eg6S4RKyauAL/Xr2XW6auqY4fZg3Bd0M6Yfb2uZj23ZTiPXmqUCxtLTB3/WwIBALMH7UIty58+ia+ikMVLN37KwZPGYTA14G4ccZDrj7V1NWwdN+vsLS1xPHfT2Ln8t1ITfl0OqKZtRkWbZsPJ9cGGD1nJDYt2iLR3tjMCBFhEVgyeVmJPEeighw/fQEHj5/BG/9AWFmYQVNTAyHvwst6WPQVq1bNFvv2bIBAAHzfZyT++ONCblmdOg448+c+zJs7FS9fvcaxY2fk7rd//x5YuOBHBAe/Q8dO/SVWOFlZWeDUyV3o0KEN5s2dkifo9duSuejerSOuXbuFQYMnICLiU8qVJi7OOHF8Bxb/byaePn2Ji5ekf6FAioHrpRSDQuWYio6ORv/+/fHmzRuJ68HBwVi/fj0MDAwgFovlyi0lEAgQGBiY5x6jR49GREQEtm7dCjs7O5iZmWHHjh35Pho2bAgAWLp0aYF1zMzM5Hpuo0aNwtChQwt8XLt2DQAwe/bs3Gs+Pvx2tawNGtUXmlqaWL90m0RQCgB8X/lh+ui5EIlEGDVlSLHu88van2FfsxpmT1iE2JjCfQNanLb0degyvCs0tDRwcMUBiaAUAAT6BGLF+OUQiUToNbG33H3a16sO94EdEBsRg6Wjl0gEpQDA+4E3fl+4HUpKSug4uJNE2Zhfx0EoFGLTzI0SQSkASE1Oxfb5W3H3sieqfWMHxxYNCvlsqSLrNaIHNLQ0sHP5bomgFAD4e/tj0djFEIlEGDhpgNx9Nmnrgmo1q8Lr1gNsWrRFIigFAOHB4Vj50+qPdSVPG9bV14Wauhreh34o4jMikt+aLbvhFxAE9zbNcWzXBpibVirrIdFXbtLEEdDW1sL8BSskglIA8Py5N/oPGAuRSITZsybJ3adAIMD8edOQmJiElq2759l2FxLyDkOHZadh+WFQb4kFBg2d62P0qEF4/z4C3/cZKRGUAgDPu16YMm0+lJSUMG7skEI+WyqvREV80JdVZium+vTpAw0NDal1JkyYgPHjx+f++cCBAzh8+DAuXLiAAwcOoFOn7A8zP//8M1JTU7FixQrUqlULAwcOlGsMWlp5V46sWLECDx48wKhRo7Bhwwbo6+tj+PDh+ba/du0a7t+/j759+xZ7K9/+/fuRmpoqs94ff/yR+/OQIUOkrtai0qWpqYEuvTsiLTUNR/fmn0T/iddzPHnwHI4N66JB4/p4eO9xoe8zcvJguHdti43LtuP6xb/w0/+mfJG29HVQ11RHq56tkZ6ajov7L+Rbx+ehN14/9IGDc03UalQbL/99IbPfus3qQklJCVePXkVKYkq+dV7cew4AsKtrl3tNW08bllUtkRiXiLuXPQvs/8qhy3D5tgm6j+mJR38VLWcQVSwamupo37Md0lPTcXrf2XzrvHz4Ci8fvkId59r4plEdPPv3ucx+LW0t4P3YB2cPniuwzrvA7JNxK1lIBgJMzI0BAO9D38v7NIiKbGDvbujUvhUqW1mU9VCIoKWliUEDeyE1NRVbt+W/ffruvQe4d+8hmjRxhmuzRrjzt+y8rF27usPOrgrWrfsdgYEh+dZ58cIHyqqWea63atUMSkpK2L3nCBISEvNte/v2XQCAk1M9mWMhxSCSvvmJyokyC0zlt1rpvyIjIyX+PGnSJOjp6WH06NHo2rUrtm/fjuHDh6N///6Ijo7G2LFjIRQK0bp16yKPy8nJCU5OnxJEfqmtfCkp+X9w+/333zF69GjUqVMHz549y9OfPL9HKh2N3ZyhraOFOzc8kZqSVmC9W9f+hmPDumjt3rzQganmbZth4szRuHr+Jras3vnF2tLXo26zutDU0cRDjwdITy34dex14z4cnGuiUbvGcgWmrKtXBgCEvs1/0ggAuga6AICUxOTca2Y22atMI8Mi822TI/Jddm4p+7r2MsdCXwfHZo7Q0tHCvx73kSbltXz3+j3Uca4N1/ZN5QpMHdx4GAc3HpZax7l59sq9Ny/8JK4b5wamPkBbVwuDJg9EHefasK1ug5jIGAT4BOKPvaeLnJCd6HPjhsm/EpCotLVq1Qy6ujq4fPkmUlIK/vL9wsXraNLEGV06fytXYGro4L4AgN17jwAA7Oyq4PtenWFtbYnw8Pd4/sIHly/fRFJScp62tWplf4byee2XpyyHsbEhACA+PkHmWEgxMPm5YiizwNTdu3fRuHFj2RX/Y/DgwahevTrc3d0xcuRICIVCDBkyBO3bty+FUQKmpqZYtGhRvmU7duyAl5cXfv311wITkpuamhb53teuXcO4ceMwYsQIqKmp4dmzZ0Xui0qeqXn2N+O+3m+l1sspN7Uo3JJ6m6rWWLZ5Efxe+2POhPxfg6XRlr4uRmbZH5wDfaQHuXPKcz5oy3Jo5QGc3HwCESEFb2Fq/G0TAMDrR69zr73zfwcAMLc1h1BZiKzM/E/UqVK7KgBAU0cTmjqaSE7IOwGlr4uJuQmA7C170vj7BEjULyplFWUYmxmjaTsXDJs+BEkJSfnkl8r+92Jpa4nd13dATUMN3o998OTuU9jY28DVvRlc3ZvhzP6zWD17XbHGQ0RUnlhZZq/ce/FCeuqR5y+8AQCWVuZy9Vu1WvYulcDAECz7bR6mTh0NJSXJ7DT+/kEYPeZH3Lh5R+L6goXLsXzFxgJXWgFA1y7uAIB/7z+SazxU/jEspRgUMvl5kyZNcPHiRQwcOBBNmmR/sPHx8YGDg4Nc7bW1tZGQIF8U3MDAAKNHj863zMPDA15eXhgwYECJn8r36tUr9OrVC+bm5lixYgXmzZtXov1T8VX6+KEmTsapN7FRsQAAM3P5A1Na2lrYsHcFRFkiTBz8I5KT819RV9Jt6etjZJYdVE+Mlf6eGB8Tn13fXL5TQT9ICUgB2YGl3hP7ICMtA39s+7QVNjkhGW+f+6FqnWroO6UfDq48kKethrYG+k0bAJFIBCUlJZhYVkKgd4Bc46KKy/jjazlexms5Ljr7PbuogSktHS2cf3Va4tqti7exbckOhPqHSlzP2crX4js37Fm9D0e3HkN6WkZuuVsHV8xYPg1dBnXGc6+XuHKy6IdkEBGVJ5aW2Sugo2NipdaLiowGAFhZyheYqmxtiejoGEyfNgajR/+An2YuxvkL1xAW9h4ONewwadII9O/XA+fPHYBL0+/w5MmnVd7SAlIAUL9+bcyZPQlpaWlYtXqrXOOh8o/5ohSDQgamAKBp06bw8fHJPYFPVVVVrsBUaGgoxGL546YfPnzA7Nmz8y178uQJAGDZsmXQ09MrsA83Nzd07NhR7nuGh4fju+++Q1ZWFs6ePSu1byo7Bob6AICkhCSp9XL2sOsbyv/3uGzzQlSuYo3R/SYjJPBdocZVnLb09dExzN5Ol1xAHqgcyfHZK5Jytt8Vx//bu+94qvc/DuAvm1ARFdohoULSpCS3vWnQENK40f61dNtTuxSS0B4aSnsoCtcqDaMSoWFmb+f3x8m5Tmc4Rlfc9/M+PB7X9/v5fL6fo4/jfN/fz+f96T1UH8sOroCYhBg8t3ng41v2GS5HVh3Crqt7MMVhGhTbtcZND1+kfEiBlEwzaBlowfJ/M5EYk4C877lQ7akGEVGROveJNH4tfrzHFuTxnz1X+Z7dQr52Y7mstAz3fO5DVEwMikoKUNNWRT/jvkj/moGjm11QVvrP1uTFhcV4E/4WT28F4ILrJY62Am4HQkxMFH8ddcTCv+bh4bWHKC+nj9CEkMavVSvBlsRl/zjf6scSOn7k5eUgLd0MEhLiWLZ0PgYPmYDIF/8syQ4Lf4lZs+2RlJSCVf+zh6uLE/r1F+webNRIE3h5HoKEhARWrd7CFtAihPx6jTYwBQBFRUWswFTnzp0RHR1dbZ3p06fj5k3eCUx/lpmZiZ07d/Itc+zYMb7ny8rKBA5MXb9+HXZ2dsjIyMC1a9fQq9evS7xXXe4sANBsbfDLrv+7mLt4NrqodRa4/PfM79j11wFkZX4HwHx6zo+srMyPeoLtiGe/ah6Mhxth+7q9CAkIE7hfda1LGjezP83RTq29wOVzM3NwYrM7cjOZM6GayfDfjKJZ82YA/pk5VRsSUhKwWjsHo2aPQVlpGVwdj+GWtx9HuQ+vPmCb9RbM374QxpOGwngSe97A57ee4dDyAzj8wBkA+C4XJI2P5aLp6KjWQeDy2Zk5cN50jDUTqplMM77lK9+zszNrN5aLi4qxffEu1vdyCi2xfOdSTJozAS3kmmPLou2sc+eOXsC5oxf4tvfI1x92a2zRtn1bqHRSwacPSbXqFyGE/AqrV9lDQ0PwfI4Z6ZlYvnIjMjKYM6GaN5flW77Fj/OVM6f4KS9nLu8XFRWF2/HTbEGpqjZv2Q9bmxnQ790Lyspt8fnzV55tNmsmhZ07HLFwgRVKS0th77AWx1y4J2snjRPlmGocGm1g6uvXr+jSpQuWL1+OLVu2/JJr8Mvp5OPjAzMzM+zcuROrVq2q87Vyc3OxZMkSeHh4QEpKClevXmXtOkh+rQGD+8JgYO/qC/6Q/Okzdv11AKlfmMmXW8rxnwnVslVLAMDXL9XfPJuONobdEitcOXcDZ9wvCtynutYljZ+OoS56DOgpcPlvn77ixGZ3ZHxlbpUsW81MqMqZUhlfMviW40VdRx1LDiyHShcVJMYm4uCyffjwinfy0cinEbA3WQh9kz5or9YeCsqKyPyWgdAHoXgf9Q5CQkKQay2PwvxC5FazdIs0LvpGvaE7QEfg8l8+fYHzpmNI/zGWq5vVVzmzKu3He3hdZaV/x1b7HTj7zBsmE4bi9JFz1ea5+lni+09o274tOqh1oMAUIeS3MszECEOGDBC4/MePn7B85UakpDCDQa3k5fiWr5wplZzypdq2s7NzkJOTi+bNZfE8KJRnueLiYrx+HY3BgwdAW6sbz8CUQR9deHoegrpaF7x+EwNr6yWIiKScvk0NhaUah0YbmPLw8EBhYSHU1Gq2I5OQkFCNlvL9G+Li4jB8+HAkJCSgdevWuHbtGit31q8kyM9Bq03NE9Q3NnMmLaxVvW8/Ak2q3brwLaemwTz/7XP1gSm7JVYQFhaGtHQz7DiykWsZ+R+BrhUb7JGbw1ySsmbRxjrVJY2f47S1taqX8ZW5+10Hdf4zVDp2Y+bRq263PG6mLp6GqYunA0KAz7HLOLv3NMpKyqqtV1Jcgue3nnE911mrC0TFRPEp7lON+0N+b0unrKhVvcpAU+dunfiWqzwvSGBKREQYc9fYAgBO7D6J0pJSruWKCosQ/SIGA/8YgC4anVmBKTFx5qxuXvUqsZL8/2afTwghZNgf5rWql5zCTCehqanOt5y2FjMVS0py9YEpAEhK/gwtzW5cd92rqri4BAAgIsJ9ub/juiVwXLcUQkJCcNrjjA0b96CkpESgPpDGhRbINw6NMjBVUVEBNzc3tGjRAubmNXuzFBMTY00D5ebu3bsYMWKEwO2tXr0aq1evrrbcnTt3MHz4cK7nlJWVUVJSgvHjx8PNzQ2tW3Mmyc7IYD4JFmT5Hfl3hASGIy83H/r9dSEpJYGiQu7bkxuZMJ8yPbrztNo2K/99h48zqbas8XAj1v+vWbSxTnXJf9er569QkFsArX7aEJeUQEkR93Hce6g+AODv+yECty0iKgKHPUswZJIxUpNTsWfRbsRGxFRbz2qdNVoqtsTp3aeQ/pl78GC4BfN9+uFFShZNmCKfv0B+bj569esJCUkJFPMYy32HMpeoB957Xm2b5eUV6DukDzprdMZjX3/ERsXxLCvTnLlsu2rA6+zzU1Bsq4Bp/SzxNfkbz7qqWl0BADEv+e9eRQghjYW//3Pk5OTCyKgfpKQkUVhYxLXcyBHM5fq+N+4K1G5Y2EtoaXaDlmY33Lhxj2c5Tc1uAICXUW/ZjouKiuKE+z5YWkxGYmIyLGcsRHBIuEDXJo0TLeVrHBplYOru3btITEzEn3/+CSkp/nlRfiYlJYXi4mIwGAwICQnhypUrkJWVhampKQBARUUFVlZWXOsyGAxcv34deXl56Nu3L549ewY9PT18+PABxcXFmDRpEsTFxbnWVVFR4dknGRkZBAUFoUOHf2YspKamYunSpWjevDlKSkpw7do1iIqKQllZuUavl/w6BfkF8L14CxY25pgyayK8Xc9zlOnZWxu99HsgLvo9IkJeVNum2bBZ1Za5G3oV7TooY/QAcyR8+Ge2SF3qkv+uwvxCPPZ5hNFWYzDCcgR8T1znKKOu2w3d9DSQEJOAt38Lngx0+lILDJlkjIToj9gwYz2+p30XqJ6UjBSMJw1FyvtkXDrCuSy1nWo7DJ4wGHnZeXh48YHA/SFNW2F+Ie753MdEqwkYO2MMLrv7cJTR1OsOrd6aiI/5iFd/c89N8rPIoJforNEZo6aN5BmY6tC1PbrraKCstAzvXr9nHQ/1D8OoaSMwbKIJTh8+y7XuaItRaK3cGh9jPrKWIxJCSGOXl5ePU6cv48+Fc2A3dyYOHjrOUaavgR769euNV6+jEfjsb4Ha3X/AFbNnTcGC+bPhfPQka5OhqiwtJ6NdOyW8jY7jWMa34a/lsLSYjKhXbzFylAW+faufZd3k90VhqcZBuKE7UBv79u0DANja2ta4bqtWrcBgMJCTw0x66u3tDXt7e9Z5bW1tnDx5kuNr/fr1SEtLQ2FhIS5fvoylS5cCAKZMmQJ/f3+0aNECsbGxsLe351pfW1ubb7+qBqUAQFFREbdu3YKLiwsr75STkxNkZGRq/JrJr3PK7TwK8guweM0CDBs1hO2cWveu2Ou6FcLCwnA74MlRd8UGBzyI8MWoiX/8O50lhAffE9dRmF+IGatmod8I9mXEHbt1xErnVRAWFsblw5xBIqt11jgRfBKG44zYjqvrqGPSAjMU5hVgi9UmgYNSAPDwwn2UlZZhisM09BnGvgFDe7X22Hx2G6RkmuGqiw8K8/nvJkj+Wy67X0FhfiHmrraG4chBbOc6a3TGX0cdISwsjNOHznDUXeBoh4t/n8XQ8cZsx886n0PGtwyMnzUWMxdbQlxCjO28tr4Wtp7YBHFJcbjv9mAbkxdcLyL3ey7mLJ+NUdNGcMx6Hms5Gos2LEBxUTG22G8HIYQ0JYcOuyMvLx9bt6zChAkj2c5pa2vg7JljEBYWxo6dhzjq7t65Hh8/hGLq1PFsx1+/jsGNm/egoqIE32teaNeO/aH9xImjsH/vJpSVlWHu3OVs5wz66GLlioXIzc3DuPGzKSj1H1FRyy/y72p0M6aCgoLw4MEDGBsbQ0dHp8b1O3Zk5kmJj4+Hrq4uIiIiYGxszLN8eHg4jh49Cm9vb/Tq1QshISHo1asXfHz+eRKro6ODiIgILFq0CL1798b48ePh4OAAY2PjWi+9ExISQlZWVq3qkn/Pp4Rk/G/hBuw+thkHT+5CXPR7JLz/BMU2rdCztzaEhIRwdI87bl9jX24kISmBmXZTISoqCgtrM9y6ynsqMiG/2tfEL9jnsAfLD6/EGrd1SIhJwOf4FMi1loO6bjcICQnh/IGzCLjBvhxVXEIc42zGQ0RUBKOtxiDA95/zgycOgYioCPJz8jFjVfWz+Q4s2cf6/7gXcTix2R1zN9nB0eMvfIpLxOf4z1DpqoL2P3Zru+Xth8vOl+rnB0CajJSEz9hqvwOOR9Zgy/GNiI/5iKQPSZBvLQ9Nve4QEhKC535vPPL1Z6snLikOM9vJEBEVwUSr8Xh0/THrXPrXDKyfuxG7T+2Azco5mGw9Ee/ffEBRQRFUOqugc7dOKC8vx1XP6zh/jD14m/juE9bP3Yit7pvwvz0rMHvpLMTHfISwsBC6dO8CxbYKyEzLwr7V+xEfXbOE6YQQ8rv78CEBM2cvwmlvZ1y+6I5Xr6MRFxcPpbat0bevHoSEhLBl6z5cvOjLVk9SUhIODrYQFRXFnwvm4MIF9tnc1jZLccvvDAwN+yHmbQCiot7i67c0aHZXh6pqZ+Tk5MJu3kqE/B3BVm/69IkQFRXF9+852Lql+lQsVnMc6v5DIA2OQXOmGoVGF5javHkzAAiU14kbXV1dAMzk6ebm5khKSoKhoSFbmZcvX+LGjRu4ePEiXr16hU6dOuHQoUOws7PjmUBPWVkZV65cgZ+fH7Zu3QoTExO0b98ekydPxvDhw2FkZIRmzfhvYU0ap8d3nmKK6WxYLbTEACMDDPljEHKyc/Hk/jOcPn4eIYGc69aLi4px+vgFjBhvinMnLzdArwlh9/f9ECwbvQQT7Sail6Eu+pgYIC87D2EPQ3HDwxevnkdx1CkpLsEND18MGmuIW15+bOdUezA3plBQVoTxpKHVXr9qYAoAbnndxNu/32DSgsnootUFOka6KC4sRvjjMPh53UT4o7A6vFrSlD279xzzRi7E1Pnm6G3YG/2H9UNedh6CHoTA58QVRD5/wVGnpKgEl09cwdBxQ3DNy5fj/NuIaEzrb4nJNpOgb9QbXTQ6Q7KZJJI/puCez31ccLmED9HxXPvzIuglpg2YAfO5k9HHSB+ausxEvx/jEnD30j2cO3oB+bn59fozIISQ38WNG/dg0G8Eli2dj2EmRhgzehiysrLhd+sBDh8+gcf+nJucFBUV4fDhEzA3H4ejLp4c57OyvmOQ4TjY2lhi2tTx6N5dHZqa3RAdHQcXV2/s3HUYycmfOerp9+4FAGjfXhkzLCdX23cKTDUNNPupcRBi1OMWdXPmzIGnp2d9NcdiZWWFkydPIjQ0FAYGBtDR0UFkZGSt2mIwGNDX10dERASEhYUhISGBL1++oEUL5vbRT58+xZAhQwAAgwcPho2NDaZNmwZRUfYYno+PD8zMzLBz506sWrWK4zoBAQFwd3fH9evXkZ2djXv37rHyWAH1/7Oq/BnVt//Crnyk6VOVUGzoLhBSL7IruCePJaQxuf/CraG7QEi9kFI2rL4QIb+5spKUhu7CL7Ww05Ra1TuawJnCgvw69TpjSklJCRoaGvXZJKtdAJCXl4e1tTVGjRpV67aEhIRw9epVzJ07FxEREdi9ezcrKAUARkZGuHLlCvT09DjyPtWEoaEhDA0NUVxcjGfPnmHoUO4zBrp27QoxMTGu5wRRWlqKDx8+1Lo+IYQQQgghhBDSFNFCvsahXmdMkaaHZkyRpoBmTJGmgmZMkaaAZkyRpoJmTJGmoKnPmJrXybxW9VwTKJfpv6nR5ZgihBBCCCGEEEIIqQ7lmGochBu6A4QQQgghhBBCCCHkv4lmTBFCCCGEEEIIIaTJYVCWqUaBAlOEEEIIIYQQQghpcmgpX+NAgSlCCCGEEEIIIYQ0OTRjqnGgwBQhhBBCCCGEEEKaHJox1ThQYIoQQgghhBBCCCFNTgWDZkw1BhSYIoQQQgghhBBCSJNDYanGgQJThBBCCCGEEEIIaXIqKDTVKFBgihBCCCGEEEIIIU0OJT9vHCgwRQghhBBCCCGEkCaHkp83DhSYIoQQQgghhBBCSJNDS/kaBwpMEUIIIYQQQgghpMmhpXyNAwWmCCGEEEIIIYQQ0uTQUr7GQbihO0AIIYQQQgghhBBC/ptoxhQhhBBCCCGEEEKaHAaDlvI1BhSYIoQQQgghhBBCSJNDyc8bBwpMEUIIIYQQQgghpMmhHFONAwWmCF8DpDo0dBcIqbOuDImG7gIh9UJYpKF7QEjdSSkbNnQXCKkXhZ8DGroLhJBq0K58jQMFpgghhBBCCCGEENLk0FK+xoECU4QQQgghhBBCCGlyKPl540CBKUIIIYQQQgghhDQ5lGOqcaDAFCGEEEIIIYQQQpocyjHVOFBgihBCCCGEEEIIIU0O5ZhqHCgwRQghhBBCCCGEkCaHckw1DhSYIoQQQgghhBBCSJNDM6YaBwpMEUIIIYQQQgghpMmhHFONg3BDd4AQQgghhBBCCCGE/DfRjClCCCGEEEIIIYQ0ORWUY6pRoMAUIYQQQgghhBBCmhwKSzUOFJgihBBCCCGEEEJIk0PJzxuHBs0x9e7dO1y8eBFpaWm1buPNmzfw8/NDbm4u33LZ2dnw8/NDdHR0jdo/efIkvn//Xuv+CSI/Px8fP37kW6asrAzFxcW03SUhhBBCCCGEECKACjBq9UX+XfU+Y+rMmTMoKyvjem7atGmQkJAAABQVFcHMzAxv375FZGQkFBUVa3U9FxcXHDlyBK9fv4aWlhbPcu/fv8eYMWOwYsUKODk5CdR2SEgIrK2tcfXqVfj6+rKOJyYmYuvWrQK1YW5ujj/++INvmY0bN2LPnj04efIkrKysuJaZO3cuPD09ERMTg27dugl0bUIIIYQQQggh5L+KJnY0DvUemJo/fz7y8vK4nhszZgwrMLVs2TJERUVBXV0dPj4+8PHx4Sivrq6O6dOnsx1bv3492/fBwcEAgEOHDqF169Y8+/X582cAQGBgIFsboqKi2LBhA9c6p06dAsAMClWVnp4Od3d3nteqqkePHnwDU2/evMGBAwegpaUFS0tLAEBpaSny8vIgJycn0DUIIYQQQgghhBDCjmY/NQ5CjHoOIcrKykJdXR1Hjx5lHZsxYwYKCwuRlJQEISEhuLu7Y+7cuRAXF4e4uDhHG2VlZSgqKsK4ceNw/fp19g4LCdVndyEpKYnCwkKO46WlpVBWVoa0tDTi4+MhLFyzVY8PHjyAqakpzp07h2nTpnEtU15ejiFDhiAwMBBPnjyBkZER8vPz0a9fPygpKeHu3bus1ztnzhyOGVMMBgM5OTnIzMxERUUFunbtWsNXX725nczrvU1C/m1dGRIN3QVC6kWDrr8npJ6s/fK4obtASL0o/BzQ0F0gpM7EFLo0dBd+qT7KRrWqF/r5aT33hPDzS5KfN2/eHH379gUApKamIj4+Hvb29gAAR0dHbNu2DQDQp08f3L17F9LS0qy6hYWFGDlyJJ4/f47Vq1dztP1zHM3e3l6gpXzh4eHQ19cXeCnfzZs3kZ6ejuXLl9c4KAUAX79+BQC+s7gWL16MwMBArFixAkZGzF8YaWlpDBw4EK6urtizZw9WrlzJVsfMzAzFxcXIzMxEVlYWKioqAAAjRozA7du3a9xP8uspqbaDqe0YaA7qieaKLZCfnY+Elx/wyOs2ogOjat2u/uj+MBg/CMpq7dGitRzSPn1F8ttE3D52DV/eJ9fjKyBNSSs1ZRjYjkKnQdqQVmiOopwCfHkZj3DPe0gIfF2rNoWEhaBjMRTakwdBvosSRMREkZ2chrg7Yfj7+C0U53IG/6tq31cD+tbDoayrCskW0shP/Y6shG94ed4fsXdCwSiv+CV1SePVSk0ZfWxHoWOVcfz1xzhOrMM47mUxFFo/jeN3P8ZxCY9xPP2iI9r31ai2/Ri/EPguPMxxvLVWR+jbjICiRns0V1GAsIgIclLSkfHhMyK97iMpJKZWr4c0ft27q2HJYjsMMzFCmzYKyMrKRlj4Szg7e+DBw9oHRIwM+8He3gZ9DfQgJ9cCX76k4sOHBJw4eQ5Xr95CeXk5z7pmZmMxfdoEaHbvBiWl1vgQn4ioqLfY7XQE0dHvat0nQrj58jUVi9duwdvY93DdtxUD+/Zu6C6RRoiW8jUOv3xXvtu3b6OiogLm5uawtraGp6cndHR0YGtrCwcHB4wcORJnz55Fu3bt8PnzZ1haWuLJkyc4ePAg+vfvL/B13r3j/8fw/fv3Neq3k5MTmjVrBhsbGwDMBOUvXrzAwIEDAQAXL17Eq1eveNb/+++/AQBt2rThev7o0aNwdnbG1KlTsXv3brZze/bswd27d7Fu3ToMGzYMurq6rHOvX7+GqKgo5OTkoKqqCnl5ecjLy7MCW+T3ovNHH9geXAwJKQmkxH5CwqsPaKHYEj2H6kHHVB83Dl6G7/4LNWpTQloS1nsXQW9EXxTlFyE5OgGf45Kg2KEN+k8eDIPxg3B933ncPnrt17wo0mip/dEb4w4thJiUBNJik/D1VTykFVui61AdqJnq4dnBqwjYx7msmh8RCTFM8VyBjgO0UJxXiJSwdygvLUPbnp0xcPFEaE0aiHPTdyA7ifsmFwMdJmDQ0kkAgIz4L/gc8Q7Sii3RcYAmOhv1wIfHL+Bjux8VZZw3SnWpSxov1T96YyyXcdxlqA5UTfXw/OBVBNZiHJv9GMcleYVIDnuHih/jeMDiidCcNBAXeIxjmbZyqCgrR7RvEN9rfHkZz3Fs6F8zoG8zAgCQFpOElLA4MMorIKvcCt1G9oHG6L6IuRmCG/ZHwKigD9b/JePGDcdpb2c0ayaF129iEB7xEm3btMaokSYYO+YPbN22Hxs37alxu47rluCv9csBALFxHxAcEo62bVrD2HggTE0H4/bth5g42ZojX6yMjDROehzAxAmjkJeXj6iot3jzNgZdunTEzBlmmD5tAjZsdMKu3Ufq5fUTEhgchrVb9iDze3ZDd4U0crSUr3H45YGpmzdvQkVFBQMGDMCXL1+QkZGBs2fPQkZGBq1atYKNjQ20tLQwb948uLu7o7S0FBcuXMCUKVNqdJ2JEyfWW58fP36MoKAgrFu3jpWU/eDBg1i3bh3c3d1hY2ODq1ev4vz589W21bZtW45jBw4cwIoVKyAuLo7WrVtj3rx5+P79O7KysvD9+3d8//4dqampKC0txdy5cxESEsKqGx4eDj09vXp7reTXUezYFjb7HSAkJIRj8/cg4s4//44q3TrA3mMNxi42w5f3SQi98VzgdievngG9EX3xNiAK7ksOIjcjh3Wuq546FrquxIQV05H0NhGv/SPr9TWRxqtlxzYYe2ABICSEq/MPIvZ2KOucYrd2MDu5AgMXT0T6uxRE3wgWuN1hG2ai4wAtxD+JwvVFR1CcUwAAEBYTgdFyM/RbMBaT3Zfh5Kh1HLOXtCYOhOFyM+R8zsCFWbuQ8e4z65yskjwmuy9DV2MdDHSYwBEwq0td0ni17NgGY36M42vzDyKuyjhW+DGOB/wYxzE1GMcmP8bxxydR8P1pHBsuN0PfBWMxyX0ZPLmMY5k2csj9kgm/pS41ei1akw2hbzMC6e9ScMPeGWnRnzhe66i9dtAY0xep0YkIPuLLoyXS1HTt2gnenochJASYT52Lq1dvsc5pa2vA95o3HNctxdvoOFy8KPi4sLCYhI0bViIp6TNGjbFgm+HUrp0yrvh4YORIEziuW8IR9NqxfR0mThiFBw+eYubsRUhLy2Cd699PH5cvuWPL5lWIinqL23ce1eHVk/+6S9dv4cwlX7z/mIh2ym3RrJkUkj9/behukUaMZkw1Dr80XUVeXh5u3bqFyZMnQ0hICGZmZvD19YWMjAwA5lI+c3Nz5OTkwMnJCVlZWRg1ahQ0NKqfEv+zkydPwt/fn+eXq6urwG1t27YN8vLyrGV02dnZ2LNnD9TV1TFr1iwAwLlz58BgMHh+jR49GtLS0mjVqhVH+0VFRSgvL4ewsDCuXbuG8PBw5OTkoGPHjpgwYQI2bdqE+/fvY9myZQgPD4eHhwerbtVlj+T3Nsx6NCSlJXFtzzm2oBQApMR+gpv9flRUVGDUn5MFbrNTL1UMtjRFTtp3HFuwhy0oBQAfIuJwbtNJCAsLY+jsEfXyOkjT0MdmOMSlJRGw5xJbUAoA0mKTcX3RETAqKtB/0XiB25Ru3RI9pxgh91sWri04xLqZB4CK0nL477yAeP8otNZoD/U/fpp+LySEQUsmoSS/CGfMt7AFlgAg90smbv640dc2MwSq5hesS13SqOn/GMeBey6xBaUAID02Gb61HMc9phgh71sWrnMZx09+jGNFjfZQ+2kcS7aUgZikOHI+Z/zcbLW6GvcCANxfd5IjKAUA3xO/sYJdXU10Oc6TpsvB3hYyMtL4a4MTW1AKAF6/joGF5QJUVFRgzWoHgdsUEhLCX47LkJeXjyFDJ3Isu0tO/ow51osBALNmTmHL6dpHXwfz7Gbi27c0mE+dyxaUAoCg4DAsWfYXhIWFsXCBVQ1fLSHs9h87iQ8JnzDCxAgXPQ5DqQ3vtCiECKICjFp9kX/XL50xdf36dRQUFMDcnJlA++vXrwgJCUFwcDDu3buHiIgIiIqKYvz48Rg+fDiuX7+Oy5cv4+LFi1BTU4OJiQkMDAxgYGDAN38UwAxy8StTGQyrzokTJ/Dw4UPMmjULPj4+iIqKQkBAALKysnDq1CmIiYkJ1E50dDQ6duzI9dzSpUuxaNGiavukra2NiooKWFhY4PlzwWfUkIYn0UwS/ScZobS4BE9O3+NaJj4iDvGR76DauxvU+mjgXWj1eUQ0BmhDWFgYgRcfoSiPe76TdyFvAQAde9R/MnzSOIk1k4D2JEOUFZUg4tRDrmU+R7xHSuR7tOutjnZ9uiE5NLbadnWmG0NETBQvzz1GSX4R1zJ/u99ClyE9oTtzGFtATH14b8h1aoNQ99vITk7nWjc9Lhk7O87gOF6XuqTxEmsmAa0f4ziSzzj+HPkeKjUYx70EGMdhVcZx1YCYrJI8ACAnhfs45KeCTx6fSqynvPS09z9DWroZZs4wQ1FREVxcvbiWCQ4JR0hIBPr318eggQYIfPZ3te2OHz8CqqqdcfDgcSQmcs9D+eZNLETFVTiOGxsPhLCwME56nkduLvedtwMCmDMUe/fuVW1fCOFnxpQJGPOHMTq0U27orpAmgkFBpkbhlwamzp49C2VlZQwcOBB///03KyG6uLg4+vXrh927d2PWrFmsPEwLFixAQkICPD098eDBA5w8eRIuLi7Q09NDeHg432tpa2vXS59v3LgBAPD29oa3tzeEhYVRUVGBsWPHYvTo0QgICICbm1u17SQkJEBBQQEzZ84EAAwZMoSVr0pCQgLJyck4c+YM3zZERESwf/9+AP/sRhgVFcXalY/8vjQGaENKthleP3mBkqISnuVePYqAau9u6GXaR6DAlLJaewDA1/jPPMvIyDcHAJ6BK/Lf03GAFiRkpRDvH4UyPuPxw6MXaNdbHWp/6Al0Q185eyTe/yXPMkkhMSjJL0KngVoQl5Zk3fj3nDIYABB18QkAQK5TG2iM6YvmygrIT/2OtLgkxPtHobSgmKPNutQljVdNxrFKb3Wo/oJx3PGncSzbVg4AkPM5AxLNm2GAwwQo91aDglo7FKRnIz0uGRHeD7gmZH9x+hE0xvTD0A0zcGORMzLjv7Cdl1WSx/AdzM8NL8/5V/s6SNNgbDwQzZvL4u7dxygs5B4oBYBbtx+if399jBs7XKDA1JzZzB2iT3ox01CoqnaGudlYtG+vgq9fv+H1m1jcvfsY+fkFHHU1NZmfO2PjPvBsX0HhR5A2J7favhDCz0Jry4buAiGkAfzSwNTbt28hJCSEsrIyGBgYwNnZGV27dkVxcTGSkpIAAJcvX+aop6ioiOnTp+PEiRNIS0uDuLh4tddau3YtlJV5R9YTExMF2o1v/vz56Nu3L7S0tKClpQV7e3s8ffoUhw8zd9KJj4/H6dOnq20HYO5IWFlWUlKSFZgCgLCwMMyfP59vfUlJSdja2gIAunZlzn7hl3vL398fgwcPFqhv5NeSa8v8gJYSy7k8o6qUOOZ5OSXOJZ/cXN93HneOXUUGn6fzOn/0AQB8fEG74xCmylkdaXFJfMulxzKfosv+GL/VtvvjpjwtlvcukOUlZchK+Io2Wp0g01YOmR+YN98tOzKn5menZMB47XQYzB0JoZ92QP2elIrbq04g8dkbtuN1qUsaL5kf4zi9nsexzI9xnF6LcSzThllXrmMbWN/bCVEpcXx5EY+kkGi0UlOB2nB9qA3Xx4vTD3Fv3Um2NpNDY+FjsxdD1k6HzcNdSI3+hNzPmagor4CskjzaaHVEfno2bv/vOF5foi2r/yvaqTA/y755wz+o+voN82GWSjslgdrt0pU5iz8xMRm7djhi6dJ5HLtOf/z4CfPmr8Sjx4Fsxzds3I3dTkd4zrQCgPHjmOkD/g6l3JaEkN9LBc06bhR+aWBq+fLlsLe3x6VLl2BhYYGFCxcCAObMmQNPT89q69+8eROjR48W6FoWFhZ8l/KFh4cLFJgaMWIERoxg/nH19PTE7du3sW/fPtayvNmzZ2P27Nkc9fbu3YsVK1YgMDCQtXMfwMyzxW/J3oIFC7gGkzZv3oz4+H928LG3t0d8fDxCQ0NRWlrKta1mzZpV+/rIv6NlW2agqeA79ynvlfIymU8W5doKFpjKSOa+s1ml9lqdMHrRZJQWl+KuGyXKJUyVAaSi7/l8yxVkMcdjZSCLH2ExETSTl0V5SVm1M5MKfoxz2bbyrBv65sqtUPg9D33tRkF3hgkebTuH9w8jkZ/6HfJdldHHZji0JgzEFK+V8Br3F1Lf/hPkrUtd0ngJOo4Lf9E4LuQyjiuvoT7KAM8OXMHfrn4oL/7nb7TaCH2M2GkDnRkmSAl/hzdX2G/4c1LSkRn/BYrd2qONVie00erEdj7zwxekx/EOBpCmR0WFuWlOZtZ3vuUy0jMBAO1UBAtMdWivgszMLCxfNh/z5s3C/1Ztgd+tB/jy5Rs0uqnCwcEWFtMnwe/mafQbMBovX/4T1OcXkAIAHR0trF3jgOLiYuzdV7NNAAgh5FejpXyNwy8NTNna2mL79u3Yt28fLCwsWMfNzMxgYWEBIyMjAMCtW7cwadIkHD58GHPnzmWV+zmfU3BwMLy82NfbBwQEAGAGcuTleX8ITU1NBQDcv38fCxYsYDtnY2MDfX19tmNJSUlYsmQJDA0NsXjxYrZzGzduxMKFC9G6NWcyvry8fwIR5eXlGDVqFLp27QoPDw+2RJKV+vbti6lTp3Icd3FxYQtM+fj4oFmzZvD39+f7OsnvQUZeFgBQWM1yusLcArbyddHDWA82B+whJiGGS9tPIeltQp3bJE2DlBxzfBVXMx6Lc5jnm8lVPx6l5GQhJCyM4jz+QQIAKM790e6PcS7ZUgbizSRRIV4OA7tROG22Bd9eJ7DKf42Kx43Fx5CTkoH+f47DyF228Br7V53rksatpuNY6heNY6kq79elhSVIiXiHuNuhCHW7xVHn3Z0wiIiJYtyRRTB2tMDb689Zu/q10e6EqWfXQLKFNKJ9gxDhdQ+Z8V9RXloG+U5toTlpEHRnmmD6+XW4Ou8APvpHVdtH0vi1aiXYkrjsH+dbKVT/mVBeXg7S0s0gISGOZUvnY/CQCYh88c/y0rDwl5g12x5JSSlY9T97uLo4oV//UQL1d9RIE3h5HoKEhARWrd7CFtAihJDfAc2Yahx+aWBKUlIS1tbW2LZtG96+fQtNTU1kZmZi+vTpMDc3h6mpKQCwluqJiYlBQkKCZ3uxsbFwceH+JObixYsC9enly5d4+ZI9j8SQIUPYAlMFBQWYMGEChISE4OXlBWFhYaSkpODNmzdo2bIltm3bBjc3N5w+fRpDhw4FABQWMj+wOjo6wsTEBKKionB0dERAQACMjIy4BqUAwNfXF8nJnE+iEhMT2b6/d+8ezp8/jxUrVtRbYIpXn6qy7WhWL9dqrEYtnIi2qu0ELp+XlYOLW7xYM6GkZKT4lpeSZc5yqyxfG+JSEjBbMwPGs0agrLQMZ9a7w//U3Vq3R35f/f8ch1aqgicDLczMxcMtZ1gzSCSqGY8SzZnnK2dO8W07KxeMiopq2wQACdkf7f4Y54wK5o25sKgIIs8+YgssVRV48Cp0LIyh1LMLZNrIIe9bVp3qkt9Dv1qM40e1GMeFv2gcF1Z5v/7b5Sb+drnJt17MjWAMXjUVLdorQq5TG9Zsq+E7rCHZQhr+28/hb1c/tjpfX33E11cfkRj4GpM9lmPELlu4D1mJ0kLKm9ZYrF5lDw0NNYHLZ6RnYvnKjcjIYM6Eat6cf2C1xY/zlTOn+Cn/kWhfVFQUbsdPswWlqtq8ZT9sbWZAv3cvKCu3xefPX3m22ayZFHbucMTCBVYoLS2FvcNaHHPhnqydEEIaEs2Yahx+aWAKAHr3ZiYVDQsLg6amJvbu3Yvc3Fx4eHjAw8ODrez8+fM58i55enqyls79vIyuqKgI06dPx61bt/D8+XPWtb59+4aYmBhoaGiwEqsHBgbC2NgYs2fPhru7O8/+ZmdnY+bMmYiIiMDcuXOxf/9+PHjwANHR0dDX10doaCgiIyNhZWUFU1NT+Pj4QF5eHjt27GC9zh07diA3NxdOTk4wNzfHli1beF7vypUruHLlCtdzkpKSPOuVlZUhOzsbWVlZrC8TExOIiIjwrENqrrthT2j0FzyxfnpSKi5u8cL3r8ytlKWreWJfOVMq62vNtxoHgM46qrDZZ482XZSREvsJHsud8el1fPUVSaPUyVAbHftrClz+e1IqHm45g9yvzKCMlBz/nUArZ0rlfqn+RqeitBwFmbmQVmjBlgyaa7s/xnnuV2a7xTkFKM4thISsFFLC4njWKy8uRVpMMjr07w7Fbu2Q9y2rTnXJ76GToTY61HAcP6rBOJb6ReNY6qdxXBMZHz6jRXtFtFJVQeaHL2it2QFte3ZBTko6R1Cqqg8PI5H4/A06DtBCF+NeiL1VfZJr8nsYZmKEIUMGCFz+48dPWL5yI1JSmMGgVvJyfMtXzpRKTvnCtxwAZGfnICcnF82by+J5UCjPcsXFxXj9OhqDBw+AtlY3noEpgz668PQ8BHW1Lnj9JgbW1ksQEfmq2n4QQkhDaMozpsrLy+Hm5gZvb2/ExsaipKQEnTp1wqRJk7B8+XK0aNGiRu09evQIJiYmApV9/fo131RKNfXLA1O9ejG3jQ0PD0f37t2xb98+GBsbs+VViouLw9mzZzFmzBiOJXU6Ojpc283JycG4cePw5MkTuLq6soJSAHDnzh1YWVmxBbUGDRqEffv2wcHBAYWFhXBzc4O0tDRHuwcPHmTtzHf8+HFISEjA0NAQ1tbWrHxX2traCAgIwPr16yElJYXRo0eztnS2s7PDX38xl42sWrUK27dv5zszqWofqzI2NkZwcDDH8f79+yMvLw+5uZxPgvPy8ri+Jl4YAvySzu1kLnB7TdHe6ZtqVS/rx42Lsnp7vuVU1Dswy3+peWBqjIMZxjiYQUhICHdcruH6vgsoKymreWdJo3Fu2vZa1au8QVdQ4z/7T6Eb87ygN965X7MgrdACCuoq+BzJfbcmEXFRyHVi5kzJ+/pPcCjnSwYUZdtVm9enrISZr0dI5J8kvXWpSxre+VqO47wf47hVPY/jvB/juJW6Cr7UcByLiDM/RpVX895bUcacsVL5d7dlJ+ZDM34bB1RKffsJHQdoQa5L22rLkt/HsD9q9/kpOYW5666mpjrfctpaGgCAlOTqA1MAkJT8GVqa3bjuuldVcTFzx0teDzod1y2B47qlEBISgtMeZ2zYuAclJbx3ySSEkIbWVGdMFRUVYdSoUXj8+DFkZGQwcOBAiIuLIywsDFu2bMGpU6fw8OFDdOnSReA2U1JSAAC6urrVBp1qGvSqzi8PTKWnM3cPS0xMxMSJE6GgoAAfHx/Iyf3zJMjPz48VmJo3bx7f9srLy3HmzBls3LgRiYmJOHbsGOzs7ATqi729PSQlJbFgwQI8e/YMq1evxpw5c9iWD5qZmSE9PR09e/ZEz5490aNHD0hJcU7xl5KSwpQpU2BqagodHR2Ymppiw4YNmDVrFsrKynDp0iVMnTqVY8eTuiotLYWSkhI0NTUhJyeHli1bQk5ODnJycgLtXkj+HTFBr1GYWwD1vpoQlxRHCY+tzXsY6wIAXt7n/QTzZyKiIrByWoh+E42QkZwGN4cDiI/gPXOEkE9Bb1GcW4j2/TQgKimOMh7jsauxDgDg3b0Igdp9dy8cbbU7ocuQXjwDU+0NNCAuLYmEZ2/YZqN8fRkPRfV2UFBvh3f3eV9PQZ0ZZKiawLwudUnjJeg47vJjHL+vwThu82Mc8wpMtfsxjhN/Gsd2Afsg21YeLgOXICeZ926prTWZG6h8fcmc0VoZLG7Wqnm1/ZNWbPGjDs36+y/w93+OnJxcGBn1g5SUJAoLuc/iGzmCmUrC94ZgS/fDwl5CS7MbtDS74caNezzLaWp2AwC8jHrLdlxUVBQn3PfB0mIyEhOTYTljIYJDwgW6NiGENKSmOmNq8eLFePz4Mf744w+cP3+eFV8pKSnB+vXrsXv3bowfPx6RkZEQFRUs7FMZmHJwcICVldWv6jpXv+wx8rlz52BhYYFJkyYBAIYPHw4lJSV4e3vDw8MDmzZtYn2dPXsWAHMXvqrHjx8/ztbmw4cPoa2tjdmzZ6OkpAT379/Ht2/fICQkxPZV+UO0srLiOJeTk4OgoCBIS0tjwYIF6Ny5My5cuMC6hqamJg4dOgRbW1sYGBhwDUoBgJeXF0xNTaGmpgY/Pz+2mUpHjhxBp06dMHToUPz9N/9p99euXWN7zZVfCQkJXMuHhYUhNjYWwcHBuH37Ns6dO4ejR49i27ZtHMniScMpzi9C0JWnEJcUh5GlKdcyXXTV0EVPHckxn/AuNEbgtscumYJ+E42QHJ2I7RPXUlCKVKskvwivrwRATFIcupZDuZZR1u0KFT1VpMYkITmU/zbllV6ce4zy0jL0mjYEYs245wfsY8vc5TTy1AO2438fZyaK1ps1DOI88vtoTRyI5krySH+XwrYUry51SeNVkl+ENz/GsQ6Pcaz0Yxyn1WAcv/wxjnvyGcf6PMbxx6fM5UuaE3gv2eo5bQiaK7dCWmwSayymRSehvLQMrbt3gKIG75m10oot0NmoBwDg26uPAr0e0rjl5eXj1OnLkJKSgt3cmVzL9DXQQ79+vfHqdTQCnwm2vHP/AVcAwIL5syEry305rKXlZLRrp4S30XEcy/g2/LUclhaTEfXqLQYMGkNBKUJIo8Go5X+/sy9fvsDDwwNKSkq4fPky26QfcXFx7Nq1C8OHD8fr169x/fp1gdutDEx16NCh3vtcnV8WmGrdujWuXr2K5s2bw8nJCXZ2dggICICxsTH27NmDjRs3sr6qBqaqHndzc2NrU1ZWFp8+fcLy5cvx6tUrDB06FHp6erC1tWX7qtztz8jIiONcjx490KdPH0RGRsLZ2RmioqJ8cznl5eXh3LlzmDhxIm7evInU1FRMmDABVlZW6Ny5M+7evYvmzdmfeEpJSeHSpUsQFRWFiYkJ6/Vxc+3aNbbXXPnFKzDFi7+/P02l/s088PBDUX4RJq60gO5wA7ZzKt06wO7wUggLC+OWsw9HXbO1M7Hr+TEYjBvIdryzjipGzB+PorxCHLLegZy077/yJZAmJPTEXZTkF8Hof1OgPoJ9ybRit3YYf8QeQsLCCDrC+cfLeN10LAw6iO7j+rMdz0/9jqiLTyHbVh4Tjy2GRPNmrHPCoiIYsnoquhrrIDUmCXH32G9i0mKT8e5+BGTbysPcYzlkldg3deg2sg+GbZyJirJy3FrpVm91SeMW9mMcG/5vCtR+GscK3dphHJ9xPGTddMznMY5f/RjH47mM48E/xnFaTBLe/TSOQ938UJSdj0FLJ6PH1MHAT0v3e1kMxdC/ZqC0qAQ3HY6yjpcVlSBwrw9ExEUxwXUJOvTvztHf1lodMenEckjJyeL15QCkx1W/7I80DYcOuyMvLx9bt6zChAkj2c5pa2vg7JljEBYWxo6dhzjq7t65Hh8/hGLq1PFsx1+/jsGNm/egoqIE32teaNeOfQOCiRNHYf/eTSgrK8PcucvZzhn00cXKFQuRm5uHceNn49u3tHp6pYQQ8usxGBW1+vqdubm5/Xi/ngtZWe45jZctWwYAOHr0KNfz3FRuytaxY8e6d7KGhBiCJBqqAVlZWejr6+Px48dgMBgC7fzm5+eHMWPGwMXFpdqlfNnZ2dWuZ/Ty8uLIMcVLSUkJxMTEWP3Mzs5GSEgInj9/jmfPniEwMBBFRUVQVFTEqlWrsHPnTqSnp2POnDk4cuQImjVjfoDdu3cvVqxYgcDAQAwcyAwmvH//HqNGjcK7d+9YSdcrp9FduHAB06ZNqzbHVOVuf9OnT8f58+eRkJDAMVBKSkogKSmJKVOm4Pz583xfb03913NM1VUvU33MPbgYEs0kkRzzCd/iP6NF65booqsOCAF+h33gu599R0kxCXEcfuMNEVERvA+LwS6z9axz0zbMgcmcUcj8nI7Y4Lc/X46Dx7LD9f6aGqOuDN67ff6XqJnqYeyhhRBvJonUmCRkxn+BTOuWUNZVhZAQ8OzQNQTuZ9+MQVRCDMveukNYVATJYXE4PXkz23kRCTGYn1yBTgO1UJxXiOSwOFSUlKFtry6QbSOH759Scc5iB7KTOG9kJFtIY+qpVVDq1QVlRSVIjf6E/LRsKKi3g1ynNijOLcSDjd54dTmgXus2ZpQtC1A11cOYH+M47cc4lq4yjp8fuoZnXMbxkirj+CyXcWx2cgU6DtRCyY9xXF5SBqVezF0dv39KxQUe47h9v+6Y6LYEki2kkZOSjrTYZAgJC0FRoz1k28ojPy0bd9d64P09zhkmw3dYo+e0IRASFkbWx6/ISvyGitJytOzUBq26KkFIWBgfHr+A74LDTWpHvrVfHjd0F357Y8f+gdPezpCWboZXr6MRFxcPpbat0bevHoSEhLBt+wFs2ryXrY6kpCS+Z8ZCVFQUz5+HwmjIBLbzcnItccvvDPro66CoqAhRUW/x9VsaNLurQ1W1M3JycrFk6V/wPsX+uWT/vs2wX2SDpKTPePI0qNq+W81xqPPrbywKPzetvzG/ozmLViE0Mgqu+7ZiYN/e1VcgNSamIHgOosaoY6uetaqXmBFVzz2pP3p6eoiMjERQUBD69evHtUxxcTFatWqF/Px85OTk8AxgVWVgYICwsDAUFhbi/v37OHHiBF68eIGsrCxoaWlh4MCBcHR05JiYUx9+aY4pQYJSNVXfSbZ+zstkbGyMyMhIAIC8vDwmT54MS0tLmJqaIisrCwcOHICXlxdmzZrFVo9bMnJVVVUEBQVhwoQJSEtLE3htJz/FxZwfTCMjI8FgMBpkyh3h7+X9MGwduwp/zB0HzUE90dOkNwpy8hH1KBwPPW4hJohzy+bS4hI8PHkLfcYMwGNv9twRHXt2BQDIKyug/ySjaq9PgSlS1bv7EfAcsx597Uaj0yBtqJrooig7Hx8eRiLs5F0kPucMdpYVlyLs5F1ojOmHCO/7HOfLi0tx3nIndKYbo4e5IVR0VSEsJoLs5HS8PPcYfx+/jZK8Qq79KcrOh/fEjdCZbgzNcf3RSk0FCurtkP4uBR9PvUKQsy/PndXqUpc0bu/vR8B7zHoY2I1Gx0Ha6FplHIefvItPPMZx+I9xHMljHF+w3Ile042hbW4I5R/jOOfHOA7lM46TgqPhOmgp+tiORCfDHlDSYb5PZ8Ql4/XlQIS43EBJLve6d9d4IPLUA+jbjoR8FyW07dkFIqIi+J6UithboXhx5iHX10Oavhs37sGg3wgsWzofw0yMMGb0MGRlZcPv1gMcPnwCj/2fcdQpKirC4cMnYG4+DkddPDnOZ2V9xyDDcbC1scS0qePRvbs6NDW7ITo6Di6u3ti56zCSkz9z1NPvzdzIqH17ZcywnFxt3/9LgSlCyO+vtvNwBIllCNp2aWkpKirqPgtLTEwMwsLCrJlN2tq8d4+XkJCAmpoaXrx4gZSUFGhoaFTbfkpKCpo3b46FCxfCw8MDurq60NPTw+fPn/Hy5Us8f/4c58+fh4+PD/r06VPn11PVL50xVVVqaip27NjBtU58fDx8fX0xdOhQ9OzJGdHU1dVlBYISExPRqVOneumrjIwMR0Dp4MGDEBERgZGREXr06MExIIuKilhL/548eQJvb2/W0r3U1FTExcVBTU2NrU5xcTGysrLQtu0/O+pwmzHl6spc/89gMODo6AhpaWkkJiYCYCYgO3z4MHr16gUdHR1Wv4qKivD48WN8+/YNN27cwJgxY+rlZ1OJZkyRpoBmTJGmgmZMkaaAZkyRpoJmTJGmoKnPmGonzzt4w09K1ptqywgaSjE2Noa/v3+t+lHV+fPnMXHiREhKSkJUVLTaVD6mpqZ48OAB7t+/j2HDhvEtW1ZWBgkJCVRUVKBHjx7w8vKCrq4u63xGRgbmz5+Py5cvo1u3boiKiqrXzdd++a58lSpnG/Hz6NEjPHr0iOP4tGnTWIEpMTExgaJ9gqhchlfV4sWL+dapmo+qRYsW8PDwAMDMKzVv3jyOoBTAjFZWDUrxcuXKFdy7x9wppWXLllixYgXr3JIlSxASEoLIyEi8fPmSrV6rVq1gZ2eHkSPZ8xAQQgghhBBCCCGkZup5/k69ycjIAIPBEGg5XeVqs/R03jsHV8rOzoaBgQEkJCRw4cIFtGnThu18q1atcO7cOURHR+PNmzc4evQolixZUqvXwE29z5gitZebm4uCggIAzH/4+lj6V1c0Y4o0BTRjijQVNGOKNAU0Y4o0FTRjijQFTX3GlIqcVq3qCTJjSlD1uZSvrKzsl8yYEtTJkydhbW2NqVOn1mt+64aPfBAWWVlZgZKSEUIIIYQQQgghhL+K32AejpiYWL21JS4uDgUFBaSlpSE3N5dv/KByplS7du3q7fqampoAgOjo6HprE6CHr4QQQgghhBBCCGmCGLX873dWGWh684b3rK7i4mK8e/cOAKCiolJtm2VlZSguLkZ5eTnfcpWruup7ozsKTBFCCCGEEEIIIaTJYTAYtfr6nY0fPx4AcPv2bZ5lnj59ivz8fAwdOlSgVVknT56EpKQkbGxs+JaLjIwEgHrflY8CU4QQQgghhBBCCGlyKsCo1dfvzM7ODqKionB3d0deXh7XMvv37wcALFy4UKA2hw8fDoAZ7MrMzORapqCgALt27WIrX18oMEUIIYQQQgghhJAmpynOmFJSUoK1tTU+f/4MMzMzZGVlsc6VlpZi1apVuH37NrS1tVmzqyq9evUKmpqaGDZsGIqKiljHO3ToADs7O6SmpmLChAlITExkq/fx40eMHTsW79+/h4WFBczMzOr1NVHyc0IIIYQQQgghhDQ5v0Py81/h4MGDeP/+Pe7evYsOHTpg0KBBEBcXR2hoKL58+YLOnTvj+vXrrJxQlc6fP4/o6GhER0cjICAApqamrHNHjx5FRkYGfHx8oKamht69e0NZWRmJiYl49eoVSkpKMGnSJLi6utb766EZU4QQQgghhBBCCGlymuKMKQCQlJTE/fv34eLiAm1tbQQHB+PBgweQk5PDX3/9hRcvXqBLly4c9aZOnYru3btj6NChGDRoENs5ERERXL58GQ8fPsSoUaOQkZGB27dvo6CgAGPHjsXdu3fh4+MDGRmZen89QozG8FMnDWZuJ/OG7gIhddaVIdHQXSCkXtDTJNIUrP3yuKG7QEi9KPwc0NBdIKTOxBQ4gxdNSQuZrrWql533oZ57QvihpXyEEEIIIYQQQghpcmgeTuNAgSlCCCGEEEIIIYQ0OU01x1RTQ4EpQgghhBBCCCGENDkMUGCqMaB0FYQQQgghhBBCCCGkQdCMKUIIIYQQQgghhDQ5tJSvcaDAFCGEEEIIIYQQQpocSn7eOFBgihBCCCGEEEIIIU0O5ZhqHCgwRQghhBBCCCGEkCaHZkw1DhSYIoQQQgghhBBCSJNDganGgQJThBBCCCGEEEIIaXIoLNU4CDEohEhIgxESEgJAkXzSuNE4Jk0FjWXSFNA4Jk0FjWVC/juEG7oDhBBCCCGEEEIIIeS/iQJThBBCCCGEEEIIIaRBUGCKEEIIIYQQQgghhDQICkwRQgghhBBCCCGEkAZBgSlCCCGEEEIIIYQQ0iAoMEUIIYQQQgghhBBCGgQFpgghhBBCCCGEEEJIg6DAFCGEEEIIIYQQQghpEEIMBoPR0J0ghBBCCCGEEEIIIf89NGOKEEIIIYQQQgghhDQICkwRQgghhBBCCCGEkAZBgSlCCCGEEEIIIYQQ0iAoMEUIIYQQQgghhBBCGgQFpgghhBDS6NFeLoQQQgghjRMFpggh5DeRlJSErKwsnufXr18PHR0dPH36tNbXKC8vr3Xd31FAQAA2b97Mcfzbt2/Q1dXFpk2bOM5t3boVe/bsoUBGA7tw4QJ0dXVx4cKFOrdVUFCADh06YPbs2fXQs4YTFBSEmzdvcozNly9fcpS9fv06YmJi/q2uES5SUlLw5s0bFBYW/pL209LSkJSUVG/tFRcX4/HjxygqKmI7HhUVBW9vbxQUFNTbtUjj9O7dO1y8eBFpaWm1buPNmzfw8/NDbm4u33LZ2dnw8/NDdHR0jdo/efIkvn//Xuv+CSI/Px8fP37kW6asrAzFxcX0WYKQeiLa0B0ghHBXXFyMvLw85OTkICcnB9nZ2cjOzkZmZiYyMjKQkZGB9PR0fPv2DV++fMGXL18gJyfH9QaG/P4SEhJgaGgIUVFRXLhwAQYGBmzns7OzcejQIQCAjo5Ora5x+vRpbN++HTdu3EDXrl1ZxxMTE+Hp6Vnj9pYsWYIWLVpwHPfz88ONGzdw+PBhiImJ1aqvgnry5Ak2bNgATU1NmJmZsY4/ffoUL168wIQJE9jKv3r1Chs3bsSIESOwYsWKX9o3wl96ejpevHiB9PT0Ord19uxZJCcnQ0JCoh56xhQfHw8TExOe5319fdGjRw8AgJmZGd68ecOzrKA3Xrt378a1a9cQEBCAQYMGAWC+N+jq6sLCwgKnT58GAPj7+2PChAlYunQp9u3bJ+hLIvXM0dERnp6eCA4ORt++fWtcPzw8HP7+/li2bBmEhIQ4zu/atQt79+7F06dPYWhoWOf+Hjx4EKtWrcLGjRuxYcMG1vHLly9jy5YtKCsrg7W1dY3bXbt2La5evVrn/lWaOHEitm/fXm/t/dedOXMGZWVlXM9NmzaN9b5ZVFQEMzMzvH37FpGRkVBUVKzV9VxcXHDkyBG8fv0aWlpaPMu9f/8eY8aMwYoVK+Dk5CRQ2yEhIbC2tsbVq1fh6+vLOp6YmIitW7cK1Ia5uTn++OMPvmU2btyIPXv24OTJk7CysuJaZu7cufD09ERMTAy6desm0LUJIbxRYIo0Cf3796+Xpyfi4uINFtg5dOgQtm3bhoKCAhQUFKCiooJnWSEhIbRs2RJycnJo1aoVWrduDS0tLbRp0wY5OTlo3rw513q9e/eutyeiQUFBaNmyZb20RYB27dph4sSJOHz4MAYNGgRvb29MmzaNdd7Z2Rk5OTlo1qxZjW6AvL290adPHwDMD53v3r1D//794efnxzqekJCAjRs31qi/wsLCmDt3LkdgisFg4MiRI7hz5w7evXsHHx8f1jhxdXXF/Pnza3QdALC1tcXx48e5nlu2bBmOHj2KtWvXYvz48axAWOWsssGDB7OVX7p0KSoqKthuykjjd/ToUQDA1KlT663N0tJSJCQkQEZGBjIyMqzjeXl5yMvLQ0lJCetYYmJinWcvpaamws/PD+rq6qygFMD8HWYwGDA1NWUdMzQ0RNu2bXH69Gns2rXrlweASf0rLy+HjY0NXr58CX9/f3h5eUFeXp51nsFg4NKlS5CVlUW/fv3qfL20tDRs27YNEhISHO/Dy5Ytw6FDh3Ds2LFaBaa+fPlSr7P3vnz5Um9tEWD+/PnIy8vjem7MmDGswNSyZcsQFRUFdXV1+Pj4wMfHh6O8uro6pk+fznZs/fr1bN8HBwcDYH6ubd26Nc9+ff78GQAQGBjI1oaoqCjPv9GnTp0CwAwKVZWeng53d3ee16qqR48efANTb968wYEDB6ClpQVLS0sAzL8HeXl5kJOTE+gahJBaYBDSBCgoKDAA1PlLUlKywV7DixcvGOvXr2fs2rWL4ezszPDy8mL4+Pgw7t27x3j+/Dlj8+bNDAAMJycnRnl5ea2uISMjUy8/JwCM9PT0ev4JEAaDwXBzc2MICwszhIWFGV5eXgwGg8HIz89nKCoq1urfyd/fn619Pz8/hrS0NKNly5aMFy9ecO1DcnIy48KFCxzHIyIiGL169WLIysoybty4wfM1lJaWMuzs7BgAGL169WJkZGQwGAwG4/z58wwNDQ2uX23btmUAYLRu3ZrjnKOjI9+fmaurKwMAw9fXl3WsV69eDHFxcUZBQQHrWGRkJENYWJixZMkSvu2Rf8eRI0cYABhHjhypUztBQUGs8a6mpsZzjPH6MjEx4dpuTEwMAwBj48aNbMc3btzIAMAICwtjHdPX1+f696Nfv34MQT9qVX2Pr1RaWsro2LEjQ1ZWlpGfn89Wfvny5QwADBcXF4HaJ/XPysqKAYARHBxcq/qZmZmMcePGscZuYmIi69zz589r/H5/+fJlntdasGDBL/vbX/lzSEhIqNXPoVJCQgIDAMPKyqpO7RB2MjIyDD09PUZwcDDrS1VVlaGiosKoqKhgMBgMxvHjxxkAGOLi4gwZGRmOL0lJSQYAxrhx4zjar6/PldV9Fi8pKWEoKCgwOnbsWKvPwffv32cAYJw7d45nmbKyMsagQYMYABhPnjxhMBgMRl5eHkNbW5thamrK+nkxGP+M+5iYGNaxiooKxvfv3xnx8fGM9+/f17iPhPyX0Ywp0iTUZS3876JXr17o1asXz/NxcXEAACkpKQgL1y49XHXr/b28vGBlZYWtW7di3bp1tboGqZu5c+dCXFwcf/75JzQ1NQEADg4OSEtLw5YtW+Do6Fin9keNGoUbN25g5MiRmDZtGt68ecMxntauXQtvb29cuHABLi4uaNGiBTZv3oxdu3aha9euCA4OZvWNG1FRUbi6ukJZWRkbN26EqakpHj16hKlTp/Kc0XLixAnY2tpi7dq1WLx4cbWvY/Xq1cjIyADAzPPQt29f+Pr6wtfXFwwGA69evYKsrCwcHBzY6unp6SE1NZX1tLVVq1bYuXNntdcjNZOYmIhOnToJVHbRokVYtGgR3zIJCQno2LEj13POzs6s/3/37p3AfaxUH7ONGAwG16VYgsrOzsb+/fshLy8POzs71vGTJ08iMTERixYtQrNmzdjqODg4wNnZGRs2bIClpSXbrC7SOMjJyeH69evYsmUL/vrrLwwaNAhPnjxB586dWbnXOnXqBElJSYHak5WV5Xrc398fbm5ukJOT4/n+evnyZbx+/RpLlizhOxv653FYG3fv3kVYWBjmz5+PVq1a1bk9Ur3mzZuzZlunpqYiPj4e9vb2AJhLUrdt2wYA6NOnD+7evQtpaWlW3cLCQowcORLPnz/H6tWrOdpm/JRjyd7eXqClfOHh4dDX1xd4Kd/NmzeRnp6O5cuX1+pz8NevXwGA7yyuxYsXIzAwECtWrICRkREAQFpaGgMHDoSrqyv27NmDlStXstUxMzNDcXExMjMzkZWVxVrxMGLECNy+fbvG/STkv4oCU4Q0sNjYWGhoaAhcXpCbOID5QfTnZUykcZg9ezaGDRsGFRUVeHt748SJE+jcuXO95UQyNjbGxYsXoaSkxPXDnaenJwwMDLB69Wpoa2ujVatW+PDhA9asWYN169YJnMdnw4YNKCgowNu3byElJVUvfa904cIFJCQksB0LCQlh+z47O5vr1P6wsDDW/3fq1IkCU7+AmJhYte9rWVlZ+PbtG9q0aVPt8ghewaNHjx7h7NmzUFRURExMDNtSqOpU3hRVXSJXW9nZ2VBQUKh1/f379yMrKws7duxgLcUuKSnB1q1bISEhwfVmsEOHDrC3t4eTkxPWr1+P/fv31/r6pGGtX78eCgoKuH37NpSUlPD9+3ecOnUKysrKiIuLq1PwNDIyEuPHj0d5eTnatGnDc4lUTEwMKzDFKwhcX27evIkjR47AzMyMAlMN4Pbt26ioqIC5uTmsra3h6ekJHR0d2NrawsHBASNHjsTZs2fRrl07fP78GZaWlnjy5AkOHjyI/v37C3yd6h4UvH//vkb9dnJyQrNmzWBjYwOAmaD8xYsXGDhwIADg4sWLePXqFc/6f//9NwCgTZs2XM8fPXoUzs7OmDp1Knbv3s12bs+ePbh79y7WrVuHYcOGQVdXl3Xu9evXEBUVhZycHFRVVSEvLw95eXlWYIsQIhgKTBHSwOTl5QUKOLx+/Rp37tyBiYkJ2x9EXtq3b18f3SMNREVFBW/fvsWCBQsgIiICXV1dLF26tEZt7N27l+fT7XHjxrF9/+DBA3z48IH1vaioKP7880/s378fqampmD59OlRUVNiSpGtqalabkHfXrl0oKyuDqGj9/rnhtVtOcXExunbtisLCQnz69IntqS/59ygrK1eb8NvZ2RmLFi3C+vXr8eeff9b4GmlpaZgxYwYqKirg5OTEEZQqLy9HXl4exMXFuQZGL126BIA5i7Au8vLy8OnTp1rnAfrw4QN2794NUVFR1gwGgBnY/fTpExwcHKCiosK17tq1a3H69GkcOHAARkZGmDhxYq36QBreggULsGDBAgDMf/vMzMw65w979+4dRowYgdzcXEhKSiImJqbamX38ZjpSkuem4ebNm1BRUcGAAQPw5csXZGRk4OzZs5CRkUGrVq1gY2MDLS0tzJs3D+7u7igtLcWFCxcwZcqUGl2nPt+PHj9+jKCgIKxbt46VlP3gwYNYt24d3N3dYWNjg6tXr+L8+fPVttW2bVuOYwcOHMCKFSsgLi6O1q1bY968efj+/TuysrLw/ft3fP/+HampqSgtLcXcuXPZHoSFh4dDT0+v3l4rIf9VFJgipIEpKirCyckJDAYDmZmZPMudO3cOd+7cgampKWxtbXmWa9myJURERH5FV8kvlJ2djeXLl2PNmjWsHfMUFBTQunVrrFmzBpcvX8aVK1dq1GbVGRRz5szhuvNe5Y3GiRMn+H6gO3fuHM6dO8d2bP78+QLtFPVzUCo+Ph5paWnQ1dWFuLh4tfVr4vjx40hJSYGjoyMFpZowBoOB2bNn48uXLzAyMsLs2bM5ygQGBmLIkCGYP38+jh07xnH+8uXLkJGRqXYMv3nzhvV7wu2m/uzZsygpKcGYMWM4zlW+F0dHR6N79+5cX8fcuXNRWFgISUlJ1pgNDg6Gk5MT2rZtyzdRf8uWLXH+/HkMHToUc+bMQZcuXfguCSe/j8zMTCxatAibN2+Gqqoq63hSUhIOHToEOTm5Gi0R5bbcdd68eUhLS4OLiwsOHz6M9PR0njuXubq6IjQ0FDt37uQ5+4/bDT03dnZ2KC0t5Xm+Mjn2mjVrWBtorF69WuAli6T28vLycOvWLdja2kJISAhmZmZsO9r26dMH5ubm8PLyYi2vmzJlSo1m9lc6efIkOnfuzPN8bGws5s2bJ1Bb27Ztg7y8PGsZXXZ2Nvbs2QN1dXXMmjULAPfPKVWNGTMG/v7+XGfpFRUVoby8HGJiYrh27RoUFRWhqKiIjh07YsiQIejcuTM6deoEHx8f7Nu3Dx4eHqy69FmDkPpBgSlCfhOpqakCfehbvXo112Udlapb009+T8eOHcOJEydw6dIlnDhxAmZmZmjdujUiIiIgJyeH48ePQ15enpVXKTs7G66urli8eDHH0rrx48fjxo0bbB/y+/fvz9ouuqKiAhcvXoSkpCTHLJMPHz6wfcjq0aMH1NTU2IJiqamp6NmzJ8drmD59OiuHQyU7OzuOHXy2bNkCT09PvnmDaqO4uBg7d+6EpKQk28wT0vQcOHAAt2/fRufOnXHx4sUa14+MjMSHDx8wfvz4aoOjly5dwqVLlyAnJ4cBAwaw7QAbFxeHNWvWQEFBgbW8pKpu3brh2bNnrJxs06ZNY7txOnDgAB4/fsxWJykpCebm5igvL4eLi0u1yxONjIywY8cO/O9//4OxsTHu3r3L2nGT/DumTp1a7XLlRYsWsc0MPH36NM6dO4dbt27h9OnTrMDm+vXrUVRUBCcnJ2hqamLGjBkC9YHbzbGTkxPCw8NhZ2eHw4cPo2XLllzHKcCcNRsaGopp06bV+X351KlTKCoqqrbc1atXWf9vZWUlcF46UnvXr19HQUEBzM3NATDzLoWEhCA4OBj37t1DREQEREVFMX78eAwfPhzXr1/H5cuXcfHiRaipqcHExAQGBgYwMDCo9rNmnz59+JYRNC/eiRMn8PDhQ8yaNQs+Pj6IiopCQEAAsrKycOrUKYFnFUZHR/Mc20uXLsWiRYuq7ZO2tjYqKipgYWGB58+fC3RdQohgKDBFyG9m0KBBXGdEBQYGwt3dHTNmzMCwYcM4zp86dQoPHz78N7pIfoHVq1ejU6dOmD9/PqZMmYKtW7di7dq1rNw7mZmZbE/5XFxcsHr1apw6dQoXL15km41RUFDAcZNkZ2fHSqrs5OSEs2fP4tixY6wp8ZXatGnDdoMjIiICcXFxnjkZqpKTk2MFv9LS0vDkyROMGDGihj8J/p48eYIhQ4ZUW06Q/lZFOdnqX35+vkA3HtXlzeP2b2NkZAQNDQ1cv369xv/WAKCrq4uoqChWklp+pk6dil69eiE0NBShoaFITk5mnTt06BBycnJw6dIlrgGkTZs2ISsrCzExMWAwGFBWVmade/jwIVauXAlVVVVkZ2cjNzcXOTk5+OOPP5CcnIxFixZh/PjxAr2elStXoqCgABs3bsTWrVtx/fp1geqR+pGYmFhtmfT0dLbvHRwc0KJFC8ybNw/jx4+Hm5sbbGxsYGFhgczMTNYy7qFDh9a6X71790bv3r1Z3/9bS/kKCwu5Hj9+/DjmzZsHbW1tvHr1iqM9QX6OpG7Onj0LZWVlDBw4EH///TcrIbq4uDj69euH3bt3Y9asWaz31QULFiAhIQGenp548OABTp48CRcXF+jp6SE8PJzvtbS1teulzzdu3AAAeHt7w9vbG8LCwqioqMDYsWMxevRoBAQEwM3Nrdp2EhISoKCggJkzZwIAhgwZwgrUSkhIIDk5GWfOnOHbhoiICGs2euXvUlRUFC1xJaQeUGCKkN+Mqqoq12UpAODu7o5+/fpxPR8WFkaBqUZu2rRp0NXVxciRI3Hs2DFYW1uzZtFlZWWxffBZtWoVunfvjjlz5qB///54+vQpaxYTt8BUpbCwMKxbtw5WVlZcn8Rv376dbQZJXl4eEhISsGnTJrZj3Bw9epT1/4IGkLhd38XFhe2Yt7c3awZImzZtuPY7ICAAiYmJ0NLS4pqDjcFgwMfHBwwGA5MnT+ZI+l6b4AbhT1RUFFZWVjzPR0dHIyQkBH379uW6zK0St5mkvXv35thRMi8vT6BAWHl5OUpLS9GjR49qywJA9+7dsWbNGtb369atw/bt2wEwc5xYWlryTAjcrl07rktwc3NzMWXKFMjKyuLmzZusPEDNmzfHxIkTER0djVGjRvH9uVRlaGgINzc3KCsrY/LkyQLVIfUnODiYdYNfE7Nnz4a6ujpGjBiBuXPnQkREBFZWVvjjjz9+QS+Z73NV38urcnd3R1hYGLZt28YzIXld3icfPHiAhQsXwtbWFhISEnyTVJNf5+3btxASEkJZWRkMDAzg7OyMrl27ori4GElJSQCYy5x/pqioiOnTp+PEiRNIS0sTaBn+2rVr2YLxP0tMTBRoN7758+ejb9++0NLSgpaWFuzt7fH06VMcPnwYADM9wOnTp6ttB2DO+K4sKykpyTaDsHKnSH4kJSVZD48r0y7wy71FD70IERwFpkiTERoaylpnXhvHjx/HoEGD2I4ZGxvD39+/jj3jxG8L2djYWLi6unIcDwoKAsC8AeeWSPr169f120nSILp164agoCAUFRWxbsgZDAays7M5bhbGjRuH4OBgeHp6sj2Z5BWYSklJwYQJE9C1a1ccOXKE6/Urb7irysvLw8aNG+vwqgSXmpqK1NRUtmMFBQWs/9fQ0MCpU6fYzjs7O+Ps2bPQ1dXFo0ePeG51PmjQICxYsAAqKiocO+6Q+ichIYGTJ0/yPO/s7IyQkBDMnDmzVsnPqwalbt++DUtLS3h4eGDChAk860RFRcHExARTpkyBs7Nzja8JgO2G7MaNG2xBK37Onz/Pyv8kKyuLFi1awNXVleNJ+/bt21FeXo5r164hJiZGoLYrb5Dmzp0rUHny++jfvz9u376NGTNmsAKcNdmtV0ZGBrm5uQKVlZOT45nTx9/fH2FhYbC0tKz3Xfmio6NhZmYGJSUlODk5wdHRsV7bJ4Jbvnw57O3tcenSJVhYWGDhwoUAeOeh/NnNmzcxevRoga5lYWHBdylfeHi4QIGpESNGsGZfe3p64vbt29i3bx9rnM6ePZvrA9u9e/dixYoVCAwMZO3cB1T/EGPBggVcg0mbN29GfHw863t7e3vEx8cjNDSUZ041XhvQEEI4UWCKNBkFBQUCf4jnJj8/n+NYhw4dapXwsTrt2rXjeS4oKIgVhOLmwoULuHDhQr33ifw+Kp9Kjx07Fjdv3mQd9/Pz47kMg1tAqbIsg8FAYWEhxowZg5SUFGzbto0VcO3ZsyfbDo55eXlsS/mUlJSgoaHBlgfn27dvAifBrakDBw5g8eLFApdfu3YtduzYAS0tLdy7d49nUApgPnWNiIiAk5MT8vLycOjQoXrfLZD8+xgMBhwdHZGdnV3tcoqePXtCXV0drq6umD9/vsCzpnjJzs4W+O/Ozzl37t27x5b0uioRERFMnjwZDAYDADMvzIQJE7Bjxw62HIM5OTlo0aKFwAmyye9pwIABiI2NZeXKERcXF+izR0pKCmuMCCI1NZVnIPXly5cAmDupViYk58bQ0LBGO1l+/foVo0ePRnl5OW7cuMG3bfLr2draYvv27di3bx8sLCxYx83MzGBhYQEjIyMAwK1btzBp0iQcPnyYLeD9cz6n4OBgeHl5sR0LCAgAwAzk8MuRV/kQ6v79+6wdKSvZ2NhAX1+f7VhSUhKWLFkCQ0NDjs8JGzduxMKFC9G6dWuO61Sd5V1eXo5Ro0aha9eu8PDw4Pre2bdvX0ydOpXjuIuLC1tgysfHB82aNYO/v3+1uQAJIdWjT+SkyRg8eHCNPqAJ4uc/tv8GKysrrrMMvLy8YGVlhSNHjnCdXWBvb89zFgxpnNq1awcNDQ0UFhYiMTERCgoKPHdLqurdu3cQExNjyxXy6dMnvHjxAgBzKVKlEydOwNraur67/st9+PABy5Ytg6+vL9TU1PDgwQOBfjaurq4QFRXFsWPH8OLFC3h4ePyS4PN/2cePHwW6cc3KygLATIZf3XtXjx49eCY5v3r1KiIiIjBjxgyBlr7t378f/fr1g4ODA0fi8aoq8+Skp6fj3r17ePPmDd68eYNHjx6xysyePZuVLFpVVRWBgYEc7QwZMgQvX77kCILxCkpxExoaCgAcSc0rc7pRYKrxKyoqYt30d+7cGdHR0dXWmT59OtvDi+pkZmZi586dfMtw28GyqrKyMoEDU9evX4ednR0yMjJw7do12jHyNyApKQlra2ts27YNb9++haamJjIzMzF9+nSYm5vD1NQUwD8zQ8XExDg2WKkqNjaWY/l9JUE3pnj58iUrMFppyJAhbIGpgoICTJgwAUJCQvDy8oKwsDBSUlLw5s0btGzZEtu2bYObmxtOnz7NystW+R7u6OgIExMTiIqKwtHREQEBATAyMuL5vunr68uWS7DSzznQ7t27h/Pnz2PFihUUmCKkHlBgipDfTFpaGkJCQjiOf/jwAQDzpo/b+Z93QyONV0lJCXx9fXHgwAFISEiwZkts376d9eTy4cOHCAkJwZo1azg+XLVo0QK9evXC06dPWcdUVVXZku8ePnwYmzZt4phhwm16+9evX3+bG9+srCxs3boVR44cQUlJCSwtLeHr6wslJaUatePg4AAXFxfo6OjAwcEBq1at4plXhdRMSUlJjWavfvv2Dd++feNbhlfQsaKiAn/99RdERETw119/CXQ9AwMDWFhY4MyZM7h8+TLbVulVffnyBQBw5MgRVuBMTk6OY5mshIQEFi5ciA0bNiAgIACGhoasc/7+/njy5AmWLl1apyUdf//9N4SEhDhmEFQuHxEk3wv5fX39+hVdunTB8uXLsWXLll9yDX45nXx8fGBmZoadO3di1apVdb5Wbm4ulixZAg8PD0hJSeHq1ausXQdJw6tMiB8WFgZNTU3s3bsXubm58PDwgIeHB1vZ+fPnc+Rd8vT0ZC2d+3kZXVFREaZPn45bt27h+fPnrGt9+/YNMTEx0NDQYM0KDwwMhLGxMWbPng13d3ee/c3OzsbMmTMRERGBuXPnYv/+/Xjw4AGio6Ohr6+P0NBQREZGwsrKCqampvDx8YG8vDx27NjBep07duxAbm4unJycYG5uzvf37MqVK1xzAwJg2+34Z2VlZcjOzkZWVhbry8TEBCIiIjzrEEL+QYEpQn4zfn5+8PPz43l+79692Lt377/YI/JvO3fuHNvsuLdv3wIAWxDp8OHDuH79OiIiInDmzBm2J5p5eXmQlZVla1NERIQt8BIYGAhRUVHo6emxlVu7di3bTe6ePXugoKDAlsQ6Ly8Pe/bsqZfXWlMjR45ESEgIevToAWdnZxgaGmLBggWsqfqvXr3Cy5cvMWLECK7BjODgYLx//x6Ojo6YPXs2bGxsWFuq0+YB9aNbt24CzV51dnbGokWLeM4CBZiB+tatW/MMvHh5eeHNmzeYM2cO1NTUBO7jzp07ceXKFaxcuRLjx4/nut3458+fATCXuMycORM6Ojro0KEDNm3axJFzbcGCBdixYwfWrVuHx48fQ0REBGVlZazAcXUJdfkpKChAUFAQunfvzrEMqri4GAAFpho7Dw8PFBYW1mgMA8yZcvU9U7yu4uLiMHz4cCQkJKB169a4du0az80BSMOonLkWHh6O7t27Y9++fTA2NmbLqxQXF4ezZ89izJgxHAFxHR0dru3m5ORg3LhxePLkCVxdXdl2hLxz5w6srKzYglqDBg3Cvn374ODggMLCQri5ubGlEqh08OBB1s58x48fh4SEBAwNDWFtbc3Kd6WtrY2AgACsX78eUlJSGD16NOt3w87OjvXgYtWqVdi+fTvfh21V+1iVsbExgoODOY73798feXl5XHO9/ZwegRDCGwWmCPlNyMvLc/2DV8nPzw9btmzB8uXLYW5uzrNcly5dfkX3yL9o3759aNWqFSsYVLmko2pgysfHBw4ODjh69Cjy8vJw7do1SEpKIj8/HxUVFRyBqary8vIQEBAAHR0djtkfa9euZfsQ5eLigk6dOmHDhg2sY9++fatxYCozMxOSkpJ1TgR68OBBBAcHY9GiRaynkFWXnhgaGkJOTg7Xrl3juvzAwsIC79+/h6ysLPT09BAWFgZXV1e+CbNJw6lc7sdtmURiYiKWLl0KaWlpgWdLVWrXrh1sbW1x+PBhnDt3juvGGZUzpubNm4dhw4bxbU9RURG7d++Gg4MDHBwc4OzsjGXLliE4OBgLFy6Eurp6jfpX1ZUrV5CXl8d156fKvFUUmGq8Kioq4ObmhhYtWvD9286NmJgYysvLeZ6/e/cuK2m0IFavXs2Ww4yXO3fuYPjw4VzPKSsro6SkBOPHj4ebmxvXnD8ZGRkAaAlqQ6mcPZ2YmIiJEydCQUEBPj4+kJOTY5Xx8/NjBaZ4JcyvVF5ejjNnzmDjxo1ITEzEsWPHYGdnJ1Bf7O3tISkpiQULFuDZs2dYvXo15syZw/b328zMDOnp6ejZsyd69uyJHj16cN3gRUpKClOmTIGpqSl0dHRgamqKDRs2YNasWSgrK8OlS5cwdepUjl1566q0tBRKSkrQ1NSEnJwcWrZsCTk5OcjJydF7MyE1QIEpQn4DFRUVKCkpYdtZ7WeV+YGUlZX5lquoqEBZWRkldW6kHjx4gKioKGzYsIEVIHr79i1atGjBtlW3iIgInJ2dISoqiq9fv7JmfGRnZwMA3wSzDx8+RElJCcaNG1erPlZNJCqItLQ0DBs2DHv37q32Br86ffv25bkte1hYGAIDAzF//nyeOTEyMjIgJSXFmo4vIiLC2pWI/H4yMzMBcC7lq6iowMyZM5GdnY1jx46x5VMT1NKlS3H06FHs3r0bM2fO5LhJrpwxxW9Xqars7e0RGRmJo0ePIjk5Gb6+vtDR0cG+fftq3LeqKpe4WFpacpyr/F1s3rx5na5BGs7du3eRmJiIP//8k+vNNj9SUlIoLi4Gg8GAkJAQrly5AllZWVaeIBUVFbbZrlUxGAxcv34deXl56Nu3L549ewY9PT18+PABxcXFmDRpEs+bahUVFZ59kpGRQVBQEDp06MA6lpqaiqVLl6J58+YoKSnBtWvXICoqCmVl5Rq9XlI3586dw40bN1jL/IcPH46UlBTs3r0bHh4ebH/b4+LiADB34auaKkJZWZktGfrDhw+xaNEixMTEQEVFBffv30dAQADPoKOVlRXHmNyzZw+CgoJgZWWFBQsWYPPmzdi/fz8rAbmmpiYOHTpU7evz8vKCg4MD1NTU4Ofnh+PHj7POHTlyBKGhoRg6dCju3r0LAwMDnu1cu3YNCQkJHMe5HQOYnz3qeydLQv6L6M6VkN9AQEAAhgwZIlDZ5cuXY/ny5XzL8JqGTH5/O3fuRLNmzbBo0SIAzJuHmJgYSEpKYuXKlRzlKz/cVz7lrpxhEh4ejpUrV6Jfv36YPHkyq3x6ejprhtH48eNr1Ld169YhIyMDUVFRAPjvLlnp8+fPGDx4MKKjozkSh9anwsJCzJ49G5KSknx/P5KSkmqcj4o0nNjYWADgyIW2Y8cOBAQEYMSIEbVeJte5c2eYmZnhwoULuHnzJsaOHct2PiQkBHJycjUaL87Ozrh//z58fX0hLCyMU6dO8U0cXB1PT088efIEI0aM4JosvTJwR4GpxqsycGlra1vjuq1atQKDwWDtzujt7Y2YmBhWjjdtbW2um6nEx8fD3t4ehYWFuHz5MsrKyvDs2TNMmTIFw4cPx4gRIxAbGws3NzeO5d6CqBqUApgzCm/duoXv378DYM6A3L59O9echuTXad26Na5evYrOnTtjyZIlsLOzw5w5cyApKQkLCwuuuUpv3rzJlmBfX1+fLTAlKyuLT58+Yfny5Vi3bh3k5OSQn5/PMZ7j4uLw9OlTGBkZccwg7dGjB/r06YPIyEi4u7tj586dfHM55eXl4caNG7h48SJsbGxgYGAAOzs7XL9+Hb169cLdu3c53hOlpKRw6dIlDBo0CCYmJnB1dWXblbCqa9eu4dq1a1zP8evXz/z9/TFgwACaNUWIgCgwRchvQF1dneeuJpWCgoLg5eWFqVOnwtjYmG9ZyufQOD148AAPHz6Eg4MDa4bIp0+fkJ+fj/z8/BotnwsPD0d4eDjmz5/PCkzFx8fD2NgYnz59AgAsXLgQ58+f5xtgkpCQYH2oKi0txd27dyEsLIxJkybxDQhUVFQAAA4dOgQhISHs2rULNjY2Ave/JtLS0mBpaYm3b99iy5YtPHc7KywsxLt371jbYZPfC4PBQHJyMuTl5SElJYXg4GBs3rwZADBgwABWue/fv8PJyQny8vIciXprasWKFbhw4QJ27drFFpiKi4tDREREjcbK27dvYWdnh+TkZIiJiaG0tBTm5uZwc3NjS4guqA8fPsDBwQEtWrSAm5sb1zKVCa35zWAhv6+goCA8ePAAxsbGPPP28FM5SyM+Ph66urqIiIjg+/kgPDwcR48ehbe3N3r16oWQkBD06tULPj4+rDI6OjqIiIjAokWL0Lt3b4wfPx4ODg4wNjau9dI7ISEh1kMT0nBMTExQUFDA9u9YuSS+culyJT8/P4wZMwYuLi58l/IZGBjg8+fPbLO0x44dyxHo9/LywtOnT2Ftbc3zwam4uDgWLlwIW1tbtrx/2dnZCAkJwfPnz/Hs2TMEBgaiqKgIioqKGDRoEObMmYP09HTMmTMHR44c4ZkyoFu3bggKCsKoUaNgaWmJe/fuwd3dnWOFQU1zTHFTUlKCoUOHYsqUKTh//rxAdQj5r6PAFGnUpk2bxrHFbH3o1avXv/qHRElJqdo1/JKSkvDy8oKhoWG1ZX82Z84ceHp6Clze0dERjo6O1ZYbM2YMKyElqRsGg4E1a9ZAVFQUy5YtYx3v2LGjwMltKyoqsGzZMhw8eBAXLlxgy0lTXFyMyZMnIzk5GYcPH4aEhASWLl0KXV1dbNu2Dd7e3jh37hxHm1Wnru/evRu7d+8WqC/37t0DwHxKeerUKbZZW/WFwWDg2rVr+PPPP/HlyxcsXboUa9eu5Vn+8OHDKCsrq1WQgPx6QkJC6N+/P1JSUiAsLMwKbk6cOJFt2UXLli0RGhqKxMREvrOZKnOz8csnoq+vDxMTE8jJyaGwsJC1lKryd6FyKUlVlbM+KsXGxuLIkSNwc3NDSUkJJkyYgGPHjuHSpUtYtWoVBg8eDAsLC6xatQo9evQQ6GcRGxsLU1NT5Obm4vTp02jfvj1HmZycHHh7ewNgLnUhjU9l4FWQvE7c6OrqAmAmTzc3N0dSUhLH+9vLly9Zs0tevXqFTp064dChQ7Czs+O5W5iysjKuXLkCPz8/bN26FSYmJmjfvj0mT56M4cOHw8jIqM75AknD+BV5vfilDqiNn2cYGRsbIzIyEgBztt3kyZNhaWkJU1NTZGVl4cCBA/Dy8uLIFcgtGbmqqiqCgoIwYcIEpKWl1Uvai8pNKKqKjIwEg8HgmD1ICOGNAlOkUUtMTKzRtuSCatmyZb23ycvdu3dx586dastV7sx2+fJlvH//vtry/fr1Y91UKSkpQUNDo24d5UKQpVxEMJcvX0ZYWBhmzpwpcK6CtWvXIjs7G+Li4sjOzkZkZCRevHgBZWVljoS3+/btw4sXL7BmzRrWMsE+ffpgzpw5mDdvHpYuXQp9fX106NABCgoKEBMTg5CQENuH2IqKCpSXl6O0tBTFxcUoLi6GtLQ0nJ2dOfomJiaGNm3a4Pr16+jbty/evHnDkRuNW16gJUuWYMmSJRzHExIS2H4u9+7dw//+9z+8fPkS0tLSHE91PTw88OzZM9ZT1+joaDx9+hSioqI8p++Thjd9+nTcvn0bFRUVUFVVxaBBg7guzVRTU+PYwezTp0/o27cvpKWlIS4uzvrbwGsGXaU7d+5w3JycP38ezZo1w4wZMwAAUVFR8Pb2hpCQEE6dOgWAuYRlxIgRuHfvHhgMBlRUVLBz505WHXt7ewwbNgzz58/HmTNncObMGYwbNw4+Pj58b4ZSUlJgaGiI9PR0HDp0iC231Lx58/D48WM0a9YM8fHxyM3NhZKSEt98KaR2avJAp1+/fgK3a2VlhZMnTyI0NBR37tyBjo4O/vjjj1r1UV9fH3p6ejhy5AiOHj0KKSkptgTqT58+ZaUJGDx4ME6dOoVp06YJfDM+evRojB49GgEBAXB3d8fJkydx4MAB3Lt3j5XHqqra5HojDSc1NRU7duzgei4+Ph4AcPHiRa6fs3V1dVmBoMTERIH/7bnlmKpKRkaGI6A0e/ZsWFtbw8jICD169GD7XKKoqIh3796xltg9efIE3t7erKV7ADgS8Ldq1QoPHjwQaBafq6srAOaDsFevXrG1paioCACYMmUKdHR0WP0qKirC48ePAYBmaBNSAxSYIo1aUFBQQ3ehzoKDg3HgwAGBy/v7+8Pf37/acra2tqzA1Pbt27F9+/Za9pD8G4YNGwYnJyeMGjVK4DqViZ+FhYUhIyMDRUVFWFtbY8OGDRz5FSIjI9G+fXu23csql2xU5pAICwuDv78/MjIyUFRUJNBMrZ+n61dasGABrK2tWR9WxcXF6xQcrTqtH2AGBWJjY2FjY4MtW7ZwzJwRERFhW+YlLCyM7t27Y9OmTXXaIY38Wk5OTnBycqpV3Q4dOqCoqIiVJ0VSUhImJibV5u7hdpO+ePFiREZGsn6PFBQUsHfvXgDMfE6Ojo5QV1dHhw4d0KdPHyxZsgRmZmYc47R79+548uQJ7t+/j7/++guqqqrVBgVUVFQwYcIEDBw4kGM5Sffu3XHixAmUl5dDVFQUenp6rIAEqV+/6oFO5XuVvLw8rK2ta/Se/zMhISFcvXoVc+fORUREBHbv3s02e8XIyAhXrlyBnp5enWZuGBoawtDQEMXFxXj27BmGDh3KtVzXrl05fgdqorS0FB8+fKh1fVIzlbON+Hn06BEePXrEcXzatGmswJSYmFi9/a5wm4m3ePFivnWq5n1q0aIF62+/lJQU5s2bx/EQA2CmKWjbtm21/bly5QprBnjLli2xYsUK1rklS5YgJCQEkZGRHKs3WrVqBTs7O4wcObLaaxBCmIQYgq4RIYQQ0miVlZXh1atXrKUfgtYpKytDRUUFzyCVmJhYgyX2zMzMhLy8PNdzDAYDJSUlrO9FRERop0pSI6WlpdXeZJeXl/NcDsVNfe2YWlZWBiEhoRpdmxBCSM3k5uaioKAAADPYRJ8jCPl1KDBFCCGEEEIIIYQQQhoE74yghBBCCCGEEEIIIYT8QhSYIoQQQgghhBBCCCENggJThBBCCCGEEEIIIaRBUGCKEEIIIYQQQgghhDQICkwRQgghhBBCCCGEkAZBgSlCCCGEEEIIIYQQ0iAoMEUIIYQQQgghhBBCGgQFpgghhBBCCCGEEEJIg6DAFCGEEEIIIYQQQghpEBSYIoQQQgghhBBCCCENggJThBBCCCGEEEIIIaRBUGCKEEIIIYQQQgghhDQICkwRQgghhBBCCCGEkAZBgSlCCCGEEEIIIYQQ0iAoMEUIIYQQQgghhBBCGgQFpgghhBBCCCGEEEJIg6DAFCGEEEIIIYQQQghpEBSYIoQQQgghhBBCCCENggJThBBCCCGEEEIIIaRB/B+yQQkvXR8FhQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "stocks_mReturns_matrix = stocks_mReturns.corr()\n",
    "sns.heatmap(stocks_mReturns_matrix.corr(), annot = True, square = False)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "852e28b4",
   "metadata": {},
   "source": [
    "## 5.2.1 概率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0e5d5a99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9 0.49 0.500248\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "def coin_test():\n",
    "    heads = 0\n",
    "    for i in range(100):\n",
    "        if random.random() <= 0.5:\n",
    "            heads +=1\n",
    "        return heads\n",
    "def do_simu(n):\n",
    "    tests = []\n",
    "    for i in range(n):\n",
    "        tests.append(coin_test())\n",
    "    return(sum(tests)/n)\n",
    "print(do_simu(10),\n",
    "      do_simu(1000),\n",
    "      do_simu(1000000))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c35f56f",
   "metadata": {},
   "source": [
    "## 5.2.2 分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4f67381d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/px/2n08wbys0n3_zcv1v9jtmzkm0000gn/T/ipykernel_261/4191055002.py:6: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  ax = sn.distplot(data, bins = 20)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import uniform\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "# 提取随机变量\n",
    "data = uniform.rvs(size = 1000, loc = 3, scale = 10) \n",
    "ax = sn.distplot(data, bins = 20)\n",
    "ax.set(ylabel = '密度')\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8f0bb9c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 3, 3, 7, 3, 5, 2, 4, 3, 2, 8, 1, 4, 2, 5])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numpy import random\n",
    "random.binomial(n = 15, p = 0.25, size = 15)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "52071846",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import binom\n",
    "import matplotlib.pyplot as plt\n",
    "pb = binom(n = 15, p = 0.5)\n",
    "x  = np.arange(1, 21)\n",
    "pmf = pb.pmf(x)\n",
    "plt.vlines(x, 0, pb.pmf(x), lw = 10)\n",
    "plt.ylabel('概率')\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "7559b6b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07464701952887114"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import binom\n",
    "#计算二项式概率\n",
    "binom.pmf(k = 15, n = 20, p = 0.6)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "bf170fc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18492460089521545"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binom.pmf(k = 5, n = 50, p = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a5c19ffe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import poisson\n",
    "import seaborn as sn\n",
    "x = np.arange(0, 60)\n",
    "pmf = poisson.pmf(x, 10)\n",
    "plt.vlines(x, 0, pmf, lw = 4)\n",
    "plt.ylabel('概率')\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a24e87b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import expon\n",
    "x = np.linspace(1, 10, 100)\n",
    "pdf = expon.pdf(x)\n",
    "plt.plot(x, pdf, lw = 4)\n",
    "plt.ylabel('概率密度')\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "4c70453f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sn\n",
    "import scipy.stats as stats\n",
    "import math \n",
    "sn.set_style('whitegrid')\n",
    "mu = 0\n",
    "sigma = 15\n",
    "x = np.linspace(mu - 3*sigma, mu + 3*sigma, 100)\n",
    "y = stats.norm.pdf(x, mu, sigma)\n",
    "sn.lineplot(x = x, y = y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2664c7e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d1038b74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.9166757493188014"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boc_close_df = pd.read_csv('../dataFiles/boc_close.csv')\n",
    "np.mean(boc_close_df['中国银行'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "3ab0ca46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10946668354800865"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(boc_close_df['中国银行']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "00e34b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "count, bins, ignored = plt.hist(boc_close_df['中国银行'], 30)\n",
    "plt.axvline(2.916675, color = 'black', linestyle = 'dashed')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5eb89cae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShapiroResult(statistic=0.9417098164558411, pvalue=2.252341868972562e-16)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import shapiro\n",
    "shapiro(boc_close_df['中国银行'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "971015e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.95996398,  1.95996398])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "norm.ppf([.025, 0.975])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "58413dca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.03643339,  1.03643339])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm.ppf([.15, .85])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab3c0ca1",
   "metadata": {},
   "source": [
    "## 5.2.3 假设检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "93f3e30d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "af05baf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_1sampResult(statistic=-1.545209716619099, pvalue=0.14824865532762857)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [180, 170, 165, 150, 177, 168, 192, 179, 150, 140, 150, 160, 130]\n",
    "stats.ttest_1samp(a = data, popmean = 170)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "48e241bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3.11\n",
       "1    3.10\n",
       "2    3.08\n",
       "3    3.11\n",
       "4    3.08\n",
       "5    3.08\n",
       "6    3.08\n",
       "7    3.09\n",
       "8    3.08\n",
       "9    3.06\n",
       "Name: 中国银行, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boc_close = pd.read_csv('../dataFiles/boc_close.csv')['中国银行']\n",
    "boc_close[:10]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "02c6c0ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37.73694374319316\n",
      "0.0\n",
      "拒绝原假设H0\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "boc_close_10 = boc_close[:10]\n",
    "alpha = 0.05\n",
    "u = 2.9\n",
    "t, p = stats.ttest_1samp(boc_close_10, u)\n",
    "p=float(\"{:.3f}\".format(p/2)) \n",
    "print(format(t))\n",
    "print(p)\n",
    "if\tp <= alpha:\n",
    "  \tprint('拒绝原假设H0')\n",
    "else:\n",
    "    print('不拒绝原假设H0')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "70be7e68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-0.7861321966452289, pvalue=0.43948337749762834)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as stats \n",
    "# 定义每个样本的身高数组\n",
    "A_city = [180, 170, 165, 150, 177, 168, 192, 179, 150, 140, 150, 160, 130]\n",
    "B_city = [169, 181, 160, 190, 155, 160, 165, 180, 172, 150, 165, 140, 190]\n",
    "stats.ttest_ind(a = A_city, b = B_city)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5d39a257",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "早期数据样本均值: 2.9301\n",
      "近期数据样本均值: 3.0825\n",
      "第一个样本的方差: 0.0\n",
      "第二个样本的方差: 0.0028\n",
      "集合样本方差: 0.004781686868686874\n",
      "标准误差: 0.009779250348249476\n",
      "t static: 15.584016624268212\n",
      "t双尾的临界值: 2.1098155778331806\n",
      "t单尾的临界值: 1.782287555649159\n",
      "双尾的p值: 2.509138896655827e-09\n",
      "单尾的p值: 1.2545694483279135e-09\n"
     ]
    }
   ],
   "source": [
    "boc_data = pd.read_csv('../dataFiles/boc_close.csv')['中国银行']\n",
    "e_data = boc_data[:100]\n",
    "p_data = boc_data[-100:]\n",
    "e_data_bar, p_data_bar = np.mean(e_data), np.mean(p_data)\n",
    "n1, n2 = len(e_data), len(p_data)\n",
    "var_e_data = np.var(e_data, ddof = 1)\n",
    "var_p_data = np.var(p_data, ddof = 1)\n",
    "var = (((n1-1)*var_e_data)+((n2-1)*var_p_data))/(n1+n2-2)\n",
    "std_error = np.sqrt(var*(1.0/n1+1.0/n2))\n",
    "print('早期数据样本均值:', np.round(e_data_bar, 4))\n",
    "print('近期数据样本均值:', np.round(p_data_bar, 4))\n",
    "print('第一个样本的方差:', np.round(var_e_data))\n",
    "print('第二个样本的方差:', np.round(var_p_data, 4))\n",
    "print('集合样本方差:', var)\n",
    "print('标准误差:', std_error)\n",
    "t = abs(e_data_bar - p_data_bar)/std_error\n",
    "print('t static:', t)\n",
    "t_c = stats.t.ppf(q = 0.975, df = 17)\t # α=0.05时的双尾临界值\n",
    "print('t双尾的临界值:', t_c)\n",
    "t_c = stats.t.ppf(q = 0.95, df = 12)\t # α=0.05时的单尾临界值\n",
    "print('t单尾的临界值:', t_c)\n",
    "p_two = 2*(1-stats.t.cdf(x = t, df = 12))\t \t# 得到双尾的p值\n",
    "print('双尾的p值:', p_two)\n",
    "p_one = 1-stats.t.cdf(x = t, df = 12)      \t# 得到单尾的p值\n",
    "print('单尾的p值:', p_one)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6014f007",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=-0.5004257131465111, pvalue=0.6266360896639515)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "before = [76,50,80,90,86,86,76,90,78,79,93,87]\n",
    "after  = [90,79,80,89,69,79,89,88,76,76,85,92]\n",
    "stats.ttest_rel(a=before, b=after)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52f69f99",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "table = ...\n",
    "stat, p, dof, expected = chi2_contingency(table)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aec65d81",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import f_oneway\n",
    "data1, data2, ... = ...\n",
    "stat, p = f_oneway(data1, data2, ...)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aabb151b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import shapiro\n",
    "data1 = ....\n",
    "stat, p = shapiro(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b01c1c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import pearsonr\n",
    "data1, data2 = ...\n",
    "corr, p = pearsonr(data1, data2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3dfa7c97",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import mannwhitneyu\n",
    "data1, data2 = ...\n",
    "stat, p = mannwhitneyu(data1, data2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abf60ada",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import kruskal\n",
    "data1, data2, ... = ...\n",
    "stat, p = kruskal(data1, data2, ...)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95ac4826",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import friedmanchisquare\n",
    "data1, data2, ... = ...\n",
    "stat, p = friedmanchisquare(data1, data2, ...)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9780150b",
   "metadata": {},
   "source": [
    "## 5.2.4 统计工具"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "2b122f42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R_DSpend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>MarketingSpend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.2</td>\n",
       "      <td>136897.8</td>\n",
       "      <td>471784.1</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   R_DSpend  Administration  MarketingSpend     State     Profit\n",
       "0  165349.2        136897.8        471784.1  New York  192261.83"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "startups_df = pd.read_csv('../dataFiles/startups.csv')\n",
    "startups_df.head(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "0cb16bb6",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.dpi'] = 180          # 设定视图分辨率\n",
    "plt.rcParams['font.sans-serif'] = ['PingFang HK']  # 显示中文\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(startups_df.R_DSpend, startups_df.Profit)\n",
    "plt.xlabel('研发投入')\n",
    "plt.ylabel('收益')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "8cbbfeb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Profit</td>      <th>  R-squared:         </th> <td>   0.947</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.945</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   849.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 13 May 2023</td> <th>  Prob (F-statistic):</th> <td>3.50e-32</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:22:51</td>     <th>  Log-Likelihood:    </th> <td> -527.44</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    50</td>      <th>  AIC:               </th> <td>   1059.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    48</td>      <th>  BIC:               </th> <td>   1063.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>    <td> 4.903e+04</td> <td> 2537.897</td> <td>   19.320</td> <td> 0.000</td> <td> 4.39e+04</td> <td> 5.41e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R_DSpend</th> <td>    0.8543</td> <td>    0.029</td> <td>   29.151</td> <td> 0.000</td> <td>    0.795</td> <td>    0.913</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>13.727</td> <th>  Durbin-Watson:     </th> <td>   1.116</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.001</td> <th>  Jarque-Bera (JB):  </th> <td>  18.536</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.911</td> <th>  Prob(JB):          </th> <td>9.44e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.361</td> <th>  Cond. No.          </th> <td>1.65e+05</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.65e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 Profit   R-squared:                       0.947\n",
       "Model:                            OLS   Adj. R-squared:                  0.945\n",
       "Method:                 Least Squares   F-statistic:                     849.8\n",
       "Date:                Sat, 13 May 2023   Prob (F-statistic):           3.50e-32\n",
       "Time:                        21:22:51   Log-Likelihood:                -527.44\n",
       "No. Observations:                  50   AIC:                             1059.\n",
       "Df Residuals:                      48   BIC:                             1063.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const       4.903e+04   2537.897     19.320      0.000    4.39e+04    5.41e+04\n",
       "R_DSpend       0.8543      0.029     29.151      0.000       0.795       0.913\n",
       "==============================================================================\n",
       "Omnibus:                       13.727   Durbin-Watson:                   1.116\n",
       "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               18.536\n",
       "Skew:                          -0.911   Prob(JB):                     9.44e-05\n",
       "Kurtosis:                       5.361   Cond. No.                     1.65e+05\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.65e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "startups_df = pd.read_csv('../dataFiles/startups.csv')\n",
    "Y = startups_df['Profit']\t\t\t\t\t\t# 定义因变量\n",
    "x = startups_df['R_DSpend']\t\t\t\t\t\t# 定义自变量\n",
    "ax = sm.add_constant(x)\t\t\t\t\t\t\t# 为预测变量添加常数\n",
    "model = sm.OLS(Y, ax).fit()\t\t\t\t\t\t# 拟合线性回归模型\n",
    "model.summary()\t\t\t\t\t\t\t\t\t# 查看回归概要\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "075f1faf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eval_env: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1620x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize = (9, 5))\n",
    "res_fig = sm.graphics.plot_regress_exog(model,'R_DSpend',fig = fig)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "36849516",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (8, 2)   # 设定视图尺寸\n",
    "res = model.resid \t\t\t\t\t\t\t\t\t # 定义残差\n",
    "QQ_fig = sm.qqplot(res, fit = True, line = '45')\t # 绘制Q-Q图\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "76547dc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████| 4/4 [00:00<00:00,  4.50it/s]\n"
     ]
    }
   ],
   "source": [
    "import qstock as qs\n",
    "code_list = ['美的集团', '海尔智家', '格力电器', '沪深300']\n",
    "df = qs.get_price(code_list,start = '20140101',end = '20230201')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3aab9636",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>沪深300</th>\n",
       "      <th>美的集团</th>\n",
       "      <th>海尔智家</th>\n",
       "      <th>格力电器</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>2321.98</td>\n",
       "      <td>2.64</td>\n",
       "      <td>6.79</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-03</td>\n",
       "      <td>2290.78</td>\n",
       "      <td>2.41</td>\n",
       "      <td>6.50</td>\n",
       "      <td>-0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>2238.64</td>\n",
       "      <td>2.02</td>\n",
       "      <td>6.46</td>\n",
       "      <td>-0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-07</td>\n",
       "      <td>2238.00</td>\n",
       "      <td>1.98</td>\n",
       "      <td>6.40</td>\n",
       "      <td>-0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-08</td>\n",
       "      <td>2241.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.45</td>\n",
       "      <td>-0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2204</th>\n",
       "      <td>2023-01-19</td>\n",
       "      <td>4156.01</td>\n",
       "      <td>57.38</td>\n",
       "      <td>26.36</td>\n",
       "      <td>35.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2205</th>\n",
       "      <td>2023-01-20</td>\n",
       "      <td>4181.53</td>\n",
       "      <td>57.08</td>\n",
       "      <td>26.39</td>\n",
       "      <td>35.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2206</th>\n",
       "      <td>2023-01-30</td>\n",
       "      <td>4201.35</td>\n",
       "      <td>55.65</td>\n",
       "      <td>26.15</td>\n",
       "      <td>35.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2207</th>\n",
       "      <td>2023-01-31</td>\n",
       "      <td>4156.86</td>\n",
       "      <td>55.20</td>\n",
       "      <td>25.70</td>\n",
       "      <td>34.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2208</th>\n",
       "      <td>2023-02-01</td>\n",
       "      <td>4195.93</td>\n",
       "      <td>55.50</td>\n",
       "      <td>25.76</td>\n",
       "      <td>34.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2209 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date    沪深300   美的集团   海尔智家   格力电器\n",
       "0     2014-01-02  2321.98   2.64   6.79   0.19\n",
       "1     2014-01-03  2290.78   2.41   6.50  -0.36\n",
       "2     2014-01-06  2238.64   2.02   6.46  -0.84\n",
       "3     2014-01-07  2238.00   1.98   6.40  -0.87\n",
       "4     2014-01-08  2241.91    NaN   6.45  -0.77\n",
       "...          ...      ...    ...    ...    ...\n",
       "2204  2023-01-19  4156.01  57.38  26.36  35.10\n",
       "2205  2023-01-20  4181.53  57.08  26.39  35.05\n",
       "2206  2023-01-30  4201.35  55.65  26.15  35.05\n",
       "2207  2023-01-31  4156.86  55.20  25.70  34.63\n",
       "2208  2023-02-01  4195.93  55.50  25.76  34.95\n",
       "\n",
       "[2209 rows x 5 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "hsStocks_pd = pd.read_csv('../dataFiles/hs300_sStocks.csv')\n",
    "hsStocks_pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "214a1dcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>美的集团</td>       <th>  R-squared:         </th> <td>   0.706</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.706</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   5146.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 24 Apr 2023</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:25:49</td>     <th>  Log-Likelihood:    </th> <td> -8600.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  2147</td>      <th>  AIC:               </th> <td>1.720e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  2145</td>      <th>  BIC:               </th> <td>1.722e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>  -60.7234</td> <td>    1.414</td> <td>  -42.937</td> <td> 0.000</td> <td>  -63.497</td> <td>  -57.950</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>沪深300</th>     <td>    0.0260</td> <td>    0.000</td> <td>   71.735</td> <td> 0.000</td> <td>    0.025</td> <td>    0.027</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>792.084</td> <th>  Durbin-Watson:     </th> <td>   0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>2752.806</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.847</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 7.138</td>  <th>  Cond. No.          </th> <td>1.92e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.92e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   美的集团   R-squared:                       0.706\n",
       "Model:                            OLS   Adj. R-squared:                  0.706\n",
       "Method:                 Least Squares   F-statistic:                     5146.\n",
       "Date:                Mon, 24 Apr 2023   Prob (F-statistic):               0.00\n",
       "Time:                        22:25:49   Log-Likelihood:                -8600.0\n",
       "No. Observations:                2147   AIC:                         1.720e+04\n",
       "Df Residuals:                    2145   BIC:                         1.722e+04\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept    -60.7234      1.414    -42.937      0.000     -63.497     -57.950\n",
       "沪深300          0.0260      0.000     71.735      0.000       0.025       0.027\n",
       "==============================================================================\n",
       "Omnibus:                      792.084   Durbin-Watson:                   0.008\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2752.806\n",
       "Skew:                          -1.847   Prob(JB):                         0.00\n",
       "Kurtosis:                       7.138   Cond. No.                     1.92e+04\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.92e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.formula.api as sm\n",
    "simple_ols = sm.ols(formula = '美的集团 ~ 沪深300',data = hsStocks_pd).fit()\n",
    "simple_ols.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f6987ccd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>美的集团</td>       <th>  R-squared:         </th> <td>   0.966</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.966</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>1.810e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 24 Apr 2023</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:28:05</td>     <th>  Log-Likelihood:    </th> <td> -5603.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1921</td>      <th>  AIC:               </th> <td>1.121e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1917</td>      <th>  BIC:               </th> <td>1.124e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>  -11.9215</td> <td>    0.624</td> <td>  -19.105</td> <td> 0.000</td> <td>  -13.145</td> <td>  -10.698</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>沪深300</th>     <td>    0.0019</td> <td>    0.000</td> <td>    7.832</td> <td> 0.000</td> <td>    0.001</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>海尔智家</th>      <td>    1.7510</td> <td>    0.026</td> <td>   67.324</td> <td> 0.000</td> <td>    1.700</td> <td>    1.802</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>格力电器</th>      <td>    0.5907</td> <td>    0.009</td> <td>   66.819</td> <td> 0.000</td> <td>    0.573</td> <td>    0.608</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>50.159</td> <th>  Durbin-Watson:     </th> <td>   0.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  58.927</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.340</td> <th>  Prob(JB):          </th> <td>1.60e-13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.524</td> <th>  Cond. No.          </th> <td>2.41e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.41e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   美的集团   R-squared:                       0.966\n",
       "Model:                            OLS   Adj. R-squared:                  0.966\n",
       "Method:                 Least Squares   F-statistic:                 1.810e+04\n",
       "Date:                Mon, 24 Apr 2023   Prob (F-statistic):               0.00\n",
       "Time:                        22:28:05   Log-Likelihood:                -5603.0\n",
       "No. Observations:                1921   AIC:                         1.121e+04\n",
       "Df Residuals:                    1917   BIC:                         1.124e+04\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept    -11.9215      0.624    -19.105      0.000     -13.145     -10.698\n",
       "沪深300          0.0019      0.000      7.832      0.000       0.001       0.002\n",
       "海尔智家           1.7510      0.026     67.324      0.000       1.700       1.802\n",
       "格力电器           0.5907      0.009     66.819      0.000       0.573       0.608\n",
       "==============================================================================\n",
       "Omnibus:                       50.159   Durbin-Watson:                   0.036\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               58.927\n",
       "Skew:                          -0.340   Prob(JB):                     1.60e-13\n",
       "Kurtosis:                       3.524   Cond. No.                     2.41e+04\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.41e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mul_ols = sm.ols(formula = '美的集团 ~ 沪深300+海尔智家+格力电器', data = hsStocks_pd).fit()\n",
    "mul_ols.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1233c71a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
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      "text/plain": [
       "<IPython.core.display.Javascript object>"
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     "output_type": "display_data"
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     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import binom\n",
    "import matplotlib.pyplot as plt\n",
    "pb = binom(n = 15, p = 0.5)\n",
    "x  = np.arange(1, 21)\n",
    "pmf = pb.pmf(x)\n",
    "plt.vlines(x, 0, pb.pmf(x), lw = 10)\n",
    "plt.ylabel('概率')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b54d37c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Duplicate key in file PosixPath('/Users/wangdong/opt/anaconda3/lib/python3.9/site-packages/matplotlib/mpl-data/matplotlibrc'), line 261 ('font.family:  sans-serif')\n",
      "Duplicate key in file PosixPath('/Users/wangdong/opt/anaconda3/lib/python3.9/site-packages/matplotlib/mpl-data/matplotlibrc'), line 269 ('font.sans-serif: SimHei, DejaVu Sans, Bitstream Vera Sans, Computer Modern Sans Serif, Lucida Grande, Verdana, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif')\n",
      "Duplicate key in file PosixPath('/Users/wangdong/opt/anaconda3/lib/python3.9/site-packages/matplotlib/mpl-data/matplotlibrc'), line 412 ('axes.unicode_minus: False  # use Unicode for the minus symbol rather than hyphen.  See')\n",
      "Duplicate key in file '/Users/wangdong/opt/anaconda3/lib/python3.9/site-packages/matplotlib/mpl-data/matplotlibrc', line 261 ('font.family:  sans-serif')\n",
      "Duplicate key in file '/Users/wangdong/opt/anaconda3/lib/python3.9/site-packages/matplotlib/mpl-data/matplotlibrc', line 269 ('font.sans-serif: SimHei, DejaVu Sans, Bitstream Vera Sans, Computer Modern Sans Serif, Lucida Grande, Verdana, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif')\n",
      "Duplicate key in file '/Users/wangdong/opt/anaconda3/lib/python3.9/site-packages/matplotlib/mpl-data/matplotlibrc', line 412 ('axes.unicode_minus: False  # use Unicode for the minus symbol rather than hyphen.  See')\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 246,
       "width": 394
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%config InlineBackend.figure_format ='retina'\n",
    "pb = binom(n = 15, p = 0.5)\n",
    "x  = np.arange(1, 21)\n",
    "pmf = pb.pmf(x)\n",
    "plt.vlines(x, 0, pb.pmf(x), lw = 10)\n",
    "plt.ylabel('概率')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d348987",
   "metadata": {},
   "source": [
    "**中国银行** "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdec1573",
   "metadata": {},
   "source": [
    "_今日股价_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22560263",
   "metadata": {},
   "source": [
    "[这是Welcome to Python.org的链接]（https://www.python.org）"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8f3cfdc",
   "metadata": {},
   "source": [
    "![Python logo]（https://www.python.org/static/img/python-logo.png）"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebeceb0e",
   "metadata": {},
   "source": [
    "1. bank1\n",
    "2. bank2\n",
    "3. bank3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63e10627",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
